Repository: facebookresearch/ScaDiver Branch: main Commit: 96001537f9ab Files: 43 Total size: 1.0 MB Directory structure: gitextract_e3i9npyq/ ├── .gitignore ├── CHANGELOG.md ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── amass_char_info.py ├── bullet/ │ ├── __init__.py │ ├── bullet_client.py │ ├── bullet_render.py │ └── bullet_utils.py ├── data/ │ ├── character/ │ │ └── amass.urdf │ ├── motion/ │ │ ├── amass_hierarchy.bvh │ │ ├── cmu/ │ │ │ ├── expert0/ │ │ │ │ └── test.bvh │ │ │ ├── expert1/ │ │ │ │ └── test.bvh │ │ │ ├── expert2/ │ │ │ │ └── test.bvh │ │ │ ├── expert3/ │ │ │ │ └── test.bvh │ │ │ ├── expert4/ │ │ │ │ └── test.bvh │ │ │ ├── expert5/ │ │ │ │ └── test.bvh │ │ │ ├── expert6/ │ │ │ │ └── test.bvh │ │ │ ├── expert7/ │ │ │ │ └── test.bvh │ │ │ └── moe/ │ │ │ └── test.bvh │ │ └── test.bvh │ └── spec/ │ ├── spec_env_humanoid_imitation_expert0.yaml │ ├── spec_env_humanoid_imitation_expert1.yaml │ ├── spec_env_humanoid_imitation_expert2.yaml │ ├── spec_env_humanoid_imitation_expert3.yaml │ ├── spec_env_humanoid_imitation_expert4.yaml │ ├── spec_env_humanoid_imitation_expert5.yaml │ ├── spec_env_humanoid_imitation_expert6.yaml │ ├── spec_env_humanoid_imitation_expert7.yaml │ ├── spec_env_humanoid_imitation_moe.yaml │ └── test_env_humanoid_imitation.yaml ├── env_humanoid_base.py ├── env_humanoid_imitation.py ├── env_humanoid_tracking.py ├── env_renderer.py ├── render_module.py ├── rllib_driver.py ├── rllib_env_imitation.py ├── rllib_model_custom_torch.py ├── sim_agent.py └── sim_obstacle.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *,cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log # Sphinx documentation docs/_build/ # PyBuilder target/ #Ipython Notebook .ipynb_checkpoints venv/ data/learning data/temp data/screenshot ================================================ FILE: CHANGELOG.md ================================================ Initial commit ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Open Source Code of Conduct ## Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. ## Our Standards Examples of behavior that contributes to creating a positive environment include: Using welcoming and inclusive language Being respectful of differing viewpoints and experiences Gracefully accepting constructive criticism Focusing on what is best for the community Showing empathy towards other community members Examples of unacceptable behavior by participants include: The use of sexualized language or imagery and unwelcome sexual attention or advances Trolling, insulting/derogatory comments, and personal or political attacks Public or private harassment Publishing others’ private information, such as a physical or electronic address, without explicit permission Other conduct which could reasonably be considered inappropriate in a professional setting ## Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. ## Scope This Code of Conduct applies within all project spaces, and it also applies when an individual is representing the project or its community in public spaces. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at opensource-conduct@fb.com. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project’s leadership. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html [homepage]: https://www.contributor-covenant.org ================================================ FILE: CONTRIBUTING.md ================================================ # Contributing to ScaDiver We want to make contributing to this project as easy and transparent as possible. ## Pull Requests We actively welcome your pull requests. 1. Fork the repo and create your branch from `master`. 2. If you've added code that should be tested, add tests. 3. If you've changed APIs, update the documentation. 4. Ensure the test suite passes. 5. Make sure your code lints. 6. If you haven't already, complete the Contributor License Agreement ("CLA"). ## Contributor License Agreement ("CLA") In order to accept your pull request, we need you to submit a CLA. You only need to do this once to work on any of Facebook's open source projects. Complete your CLA here: ## Issues We use GitHub issues to track public bugs. Please ensure your description is clear and has sufficient instructions to be able to reproduce the issue. Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe disclosure of security bugs. In those cases, please go through the process outlined on that page and do not file a public issue. ## Coding Style * 2 spaces for indentation rather than tabs * 80 character line length ## License By contributing to ScaDiver, you agree that your contributions will be licensed under the LICENSE file in the root directory of this source tree. ================================================ FILE: LICENSE ================================================ BSD License For ScaDiver software Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name Facebook nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ================================================ FILE: README.md ================================================ # ScaDiver ## Introduction [![ScaDiver](./data/image/scadiver_video.png)](https://www.youtube.com/watch?v=QnIwwAKX5H4&t) This repository provides executable codes for the paper *A Scalable Approach to Control Diverse Behaviors for Physically Simulated Characters*, which was published in SIGGRAPH 2020. Click [here](https://research.fb.com/publications/a-scalable-approach-to-control-diverse-behaviors-for-physically-simulated-characters/) to see the paper. The name of the project originates from the two keywords in the name of the paper, which are **SCA**lable and **DIVER**se. Please note that we are not able to guarantee that the codes will work if it is run with settings that we did not intend when we developed for the project. We would appreciate it if users could report the problems when they find it. ## Citation ``` @article{ ScaDiver, author = {Won, Jungdam and Gopinath, Deepak and Hodgins, Jessica}, title = {A Scalable Approach to Control Diverse Behaviors for Physically Simulated Characters}, year = {2020}, issue_date = {July 2020}, volume = {39}, number = {4}, url = {https://doi.org/10.1145/3386569.3392381}, journal = {ACM Trans. Graph.}, articleno = {33}, } ``` ## Getting Started ### Installation Although we only confirmed that the codes work correctly in Ubuntu (18.04) environment, probably it will work in other environments without much troubles if it is installed in a fresh Python virtual environment, which we recommend users to use to prevent from mixing different pakage versions. #### ScaDiver ``` git clone https://github.com/fairinternal/ScaDiver ``` #### fairmotion [fairmotion](https://github.com/fairinternal/fairmotion) provides functionalities to process motion capture data and, to compute kinematics, to visualize simulated characters and environments. ``` pip install fairmotion ``` #### others We use [PyBullet](https://pybullet.org/wordpress/) for physics simulation and [rllib](https://docs.ray.io/en/latest/rllib.html) for reinforcement learning. ``` pip install pybullet==2.7.3 ray[rllib]==0.8.7 pandas requests ``` ### Examples #### Test Tracking Environment In this example, a humanoid character will be loaded into space where only the ground plane exists, and it will be simulated without any control. Please press **a** to simulate and press **r** to reset. ``` python env_humanoid_tracking.py ``` #### Test Imitation Environment In this example, a humanoid character will be loaded into space where only the ground plane exists, and its initial states will be set by some motion capture data specified in the specification file (test_env_humanoid_imiation.yaml). Please note that the character will fall immediately because the current controller is the one not trained at all. ``` python rllib_driver.py --mode load --spec data/spec/test_env_humanoid_imitation.yaml --project_dir ./ ``` #### Learning Experts and Mixture of Experts Controllers To learn expert controllers for a set of heterogeneous motion capture data, we first need to cluster the data according to some motion features. For this, please refer to an example in [fairmotion](https://github.com/fairinternal/fairmotion) about *motion clustering*. After clustering the data, each expert controller can be learned by using only data involved in each cluster. ``` python rllib_driver.py --mode train --spec data/spec/spec_env_humanoid_imitation_expert0.yaml --project_dir ./ --local_dir ./data/learning/experts/ python rllib_driver.py --mode train --spec data/spec/spec_env_humanoid_imitation_expert1.yaml --project_dir ./ --local_dir ./data/learning/experts/ ... python rllib_driver.py --mode train --spec data/spec/spec_env_humanoid_imitation_expert7.yaml --project_dir ./ --local_dir ./data/learning/experts/ ``` After expert controllers are learned, they can be combined as a single *mixture-of-experts* controller. In our framework, this can be simply achieved by setting the type of policy model as *moe* as below. ``` python rllib_driver.py --mode train --spec data/spec/spec_env_humanoid_imitation_moe.yaml --project_dir ./ --local_dir ./data/learning/moe/ ``` When the training process is performed, it will automatically create logs by using Tensorboard in the *local_dir/project_name*. The status can be checked by the script below. ``` tensorboard --logdir="directory_where_tensorboard_log_exists" ``` According to the iteration number specified in the specification file, it will automatically save intermediate results in the *local_dir/project_name/checkpoint/*. Testing the controllers can be done as below. ``` python rllib_driver.py --mode load --spec "some_specification_file" --project_dir ./ --checkpoint "some_saved_checkpoint_file" ``` ### Specification File Every experiment requires a unique specification file (yaml) that includes all the settings about individual experiments. Most of the elements are easy to understand by its name and it is based on **rllib**'s configuration file. We will explain only our environment-specific settings below, please refer to the documentation of **rllib** for other settings. #### Reward Because the optimality of learned policy is defined by the definition of reward function in reinforcement learning, changing the function is the only way for users to design the policy so that it has desirable properties/behaviors. As a result, testing many reward functions by combining various terms with different combinations is a critical process in many researches including ours. We implemented a flexible way to test various functions by simply modifying a specification file. Our reward function is defined in a tree-like manner, where each node could be a *operation* among child nodes or a *leaf* that defines a term. For example, the function below is a multiplicative reward function composed of the five terms (pose_pos, pose_vel, ee, root, and com), where each term has its own kernel function. Currently, we only support *gaussian* and *none* kernel functions. By simply changing the operation of the root node from *mul* into *add*, we can test an additive reward function. If we want to change weight values for the terms, we can simply change *weight* in the leaf nodes. We can also define multiple reward functions in the same specification files (please note that their names should be unique), then choose one of them according to experiments by setting the name which we want to test in *fn_map*. ``` reward: fn_def: default: name: total op: mul child_nodes: - name: pose_pos op: leaf weight: 1.0 kernel: type: gaussian scale: 40.0 - name: pose_vel op: leaf weight: 1.0 kernel: type: gaussian scale: 1.0 - name: ee op: leaf weight: 1.0 kernel: type: gaussian scale: 5.0 - name: root op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 - name: com op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 fn_map: - default ``` #### Early Termination We can use various early termination strategies. For example, we can terminate the current episode when the character falls down, or the average reward for 1s is below a specific threshold as used in our paper. ``` early_term: choices: # 'sim_div', 'sim_window', task_end', 'falldown', 'low_reward' - task_end - low_reward low_reward_thres: 0.1 eoe_margin: 0.5 ``` #### Characters The character fields include character-specific information. ``` character: name: - humanoid char_info_module: - amass_char_info.py sim_char_file: - data/character/amass.urdf ref_motion_scale: - 1.0 base_motion_file: - data/motion/amass_hierarchy.bvh ref_motion_db: - data: file: - data/motion/test.bvh actuation: - spd self_collision: - true environment_file: [] ``` #### Others If true, the environment will be fully created when *reset* is called. This is useful when the creation of the environment is expensive. Please search with the keyword *Expensive Environments* [here](https://docs.ray.io/en/latest/rllib-env.html#:~:text=Expensive%20Environments,-Some%20environments%20may&text=RLlib%20will%20create%20num_workers%20%2B%201,until%20reset()%20is%20called.) for more details. ``` lazy_creation: false ``` When the codes run, we assume that the current working directory (cwd) is the root directory where the project is originally saved. However, there exist some cases when the cwd could be changed by other external libraries such as *rllib*, so specifying *project_dir* is necessary to keep the root directory of the project. ``` project_dir: /home/jungdam/Research/opensource/ScaDiver/ ``` Time steps for physics simulation ``` fps_sim: 480 ``` Time steps for control policies ``` fps_con: 30 ``` If true, small noise will be added to th the initial state of the character ``` add_noise: false ``` If true, some additional information will be printed. Please use this only for testing environments. ``` verbose: false ``` This defines which information will be added to the state of the character. 'body' will include the physical state of the simulated character such as joint angles and joint angular velocities, 'task' will include the difference between the simulated character and the currently playing reference motion. ``` state: choices: ['body', 'task'] # 'body', 'imitation', 'interaction', 'task' ``` This includes information on the action space for our environment. Basically, the action space is a target posture that includes a set of target joint angles except for the root joint. 'range_min/range_max' defines the range of joint angles in radian for which we will use, and 'range_min_pol/range_max_pol' defines the range of output values of the policy (controller). In other words, there exists a linear mapping from (range_min_pol, range_max_pol) to (range_min, range_max). For example in the setting below, the full range of motion of our simulated character is between -3.0 radian and 3.0 radians from its base posture (t-pose), and if the policy outputs 3.0 for a specific joint, the environment will interpret the value as 3.0 radians. This feature is especially useful when we want to restrict the range of motion of the simulated character (range_min/range_max will be useful), or the policy has special output layers such as *tanh* (range_min_pol/range_max_pol will be useful). ``` action: type: "absolute" # 'absolute', 'relative' range_min: -3.0 range_max: 3.0 range_min_pol: -3.0 range_max_pol: 3.0 ``` ## Using Clusters Because our framework is based on *rllib*, it can run not only on a single machine with multiple cores but also on clusters such as AWS, Azure, or customized one managed by Slurm. Please refer to [this](https://docs.ray.io/en/latest/cluster/index.html) for more detail. ## Pretrained Model To be updated. ## License ScaDiver is released under the [BSD-3-Clause License](https://github.com/fairinternal/ScaDiver/blob/master/LICENSE). ================================================ FILE: amass_char_info.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. import collections import numpy as np ''' The up direction of the character w.r.t. its root joint. The up direction in the world frame can be computed by dot(R_root, v_up), where R_root is the orientation of the root. ''' v_up = np.array([0.0, 1.0, 0.0]) ''' The facing direction of the character w.r.t. its root joint. The facing direction in the world frame can be computed by dot(R_root, v_face), where R_root is the orientation of the root. ''' v_face = np.array([0.0, 0.0, 1.0]) ''' The up direction of the world frame, when the character holds its defalult posture (e.g. t-pose). This information is useful/necessary when comparing a relationship between the character and its environment. ''' v_up_env = np.array([0.0, 0.0, 1.0]) ''' Definition of Link/Joint (In our character definition, one joint can only have one link) ''' root = -1 lhip = 0 lknee = 1 lankle = 2 rhip = 3 rknee = 4 rankle = 5 lowerback = 6 upperback = 7 chest = 8 lowerneck = 9 upperneck = 10 lclavicle = 11 lshoulder = 12 lelbow = 13 lwrist = 14 rclavicle = 15 rshoulder = 16 relbow = 17 rwrist = 18 ''' Definition of the root (base) joint ''' ROOT = root ''' Definition of end effectors ''' end_effector_indices = [ lwrist, rwrist, lankle, rankle, ] ''' Mapping from joint indicies to names ''' joint_name = collections.OrderedDict() joint_name[root] = "root" joint_name[lhip] = "lhip" joint_name[lknee] = "lknee" joint_name[lankle] = "lankle" joint_name[rhip] = "rhip" joint_name[rknee] = "rknee" joint_name[rankle] = "rankle" joint_name[lowerback] = "lowerback" joint_name[upperback] = "upperback" joint_name[chest] = "chest" joint_name[lowerneck] = "lowerneck" joint_name[upperneck] = "upperneck" joint_name[lclavicle] = "lclavicle" joint_name[lshoulder] = "lshoulder" joint_name[lelbow] = "lelbow" joint_name[lwrist] = "lwrist" joint_name[rclavicle] = "rclavicle" joint_name[rshoulder] = "rshoulder" joint_name[relbow] = "relbow" joint_name[rwrist] = "rwrist" ''' Mapping from joint names to indicies ''' joint_idx = collections.OrderedDict() joint_idx["root"] = root joint_idx["lhip"] = lhip joint_idx["lknee"] = lknee joint_idx["lankle"] = lankle joint_idx["rhip"] = rhip joint_idx["rknee"] = rknee joint_idx["rankle"] = rankle joint_idx["lowerback"] = lowerback joint_idx["upperback"] = upperback joint_idx["chest"] = chest joint_idx["lowerneck"] = lowerneck joint_idx["upperneck"] = upperneck joint_idx["lclavicle"] = lclavicle joint_idx["lshoulder"] = lshoulder joint_idx["lelbow"] = lelbow joint_idx["lwrist"] = lwrist joint_idx["rclavicle"] = rclavicle joint_idx["rshoulder"] = rshoulder joint_idx["relbow"] = relbow joint_idx["rwrist"] = rwrist ''' Mapping from character's joint indicies to bvh's joint names. Some entry could have no mapping (by assigning None). ''' bvh_map = collections.OrderedDict() bvh_map[root] = "root" bvh_map[lhip] = "lhip" bvh_map[lknee] = "lknee" bvh_map[lankle] = "lankle" bvh_map[rhip] = "rhip" bvh_map[rknee] = "rknee" bvh_map[rankle] = "rankle" bvh_map[lowerback] = "lowerback" bvh_map[upperback] = "upperback" bvh_map[chest] = "chest" bvh_map[lowerneck] = "lowerneck" bvh_map[upperneck] = "upperneck" bvh_map[lclavicle] = "lclavicle" bvh_map[lshoulder] = "lshoulder" bvh_map[lelbow] = "lelbow" bvh_map[lwrist] = "lwrist" bvh_map[rclavicle] = "rclavicle" bvh_map[rshoulder] = "rshoulder" bvh_map[relbow] = "relbow" bvh_map[rwrist] = "rwrist" ''' Mapping from bvh's joint names to character's joint indicies. Some entry could have no mapping (by assigning None). ''' bvh_map_inv = collections.OrderedDict() bvh_map_inv["root"] = root bvh_map_inv["lhip"] = lhip bvh_map_inv["lknee"] = lknee bvh_map_inv["lankle"] = lankle bvh_map_inv["ltoe"] = None bvh_map_inv["rhip"] = rhip bvh_map_inv["rknee"] = rknee bvh_map_inv["rankle"] = rankle bvh_map_inv["rtoe"] = None bvh_map_inv["lowerback"] = lowerback bvh_map_inv["upperback"] = upperback bvh_map_inv["chest"] = chest bvh_map_inv["lowerneck"] = lowerneck bvh_map_inv["upperneck"] = upperneck bvh_map_inv["lclavicle"] = lclavicle bvh_map_inv["lshoulder"] = lshoulder bvh_map_inv["lelbow"] = lelbow bvh_map_inv["lwrist"] = lwrist bvh_map_inv["rclavicle"] = rclavicle bvh_map_inv["rshoulder"] = rshoulder bvh_map_inv["relbow"] = relbow bvh_map_inv["rwrist"] = rwrist ''' Definition of PD gains (tuned for Stable PD Controller) ''' kp = { root : 0, lhip : 500, lknee : 400, lankle : 300, rhip : 500, rknee : 400, rankle : 300, lowerback : 500, upperback : 500, chest : 500, lowerneck : 200, upperneck : 200, lclavicle : 400, lshoulder : 400, lelbow : 300, lwrist : 0, rclavicle : 400, rshoulder : 400, relbow : 300, rwrist : 0, } kd = {} for k, v in kp.items(): kd[k] = 0.1 * v kd[root] = 0 ''' Definition of PD gains (tuned for Contrained PD Controller). "cpd_ratio * kp" and "cpd_ratio * kd" will be used respectively. ''' cpd_ratio = 0.0002 max_force = { root : 0, lhip : 300, lknee : 200, lankle : 100, rhip : 300, rknee : 200, rankle : 100, lowerback : 300, upperback : 300, chest : 300, lowerneck : 100, upperneck : 100, lclavicle : 200, lshoulder : 200, lelbow : 150, lwrist : 0, rclavicle : 200, rshoulder : 200, relbow : 150, rwrist : 0 , } ''' Maximum forces that character can generate when PD controller is used. ''' contact_allow_map = { root : False, lhip : False, lknee : False, lankle : False, rhip : False, rknee : False, rankle : False, lowerback : False, upperback : False, chest : False, lowerneck : False, upperneck : False, lclavicle : False, lshoulder : False, lelbow : False, lwrist : False, rclavicle : False, rshoulder : False, relbow : False, rwrist : False, } joint_weight = { root : 1.0, lhip : 0.5, lknee : 0.3, lankle : 0.2, rhip : 0.5, rknee : 0.3, rankle : 0.2, lowerback : 0.4, upperback : 0.4, chest : 0.3, lowerneck : 0.3, upperneck : 0.3, lclavicle : 0.3, lshoulder : 0.3, lelbow : 0.2, lwrist : 0.0, rclavicle : 0.3, rshoulder : 0.3, relbow : 0.2, rwrist : 0.0, } ''' mu, sigma, lower, upper ''' noise_pose = { root : (0.0, 0.1, -0.5, 0.5), lhip : (0.0, 0.1, -0.5, 0.5), lknee : (0.0, 0.1, -0.5, 0.5), lankle : (0.0, 0.1, -0.5, 0.5), rhip : (0.0, 0.1, -0.5, 0.5), rknee : (0.0, 0.1, -0.5, 0.5), rankle : (0.0, 0.1, -0.5, 0.5), lowerback : (0.0, 0.1, -0.5, 0.5), upperback : (0.0, 0.1, -0.5, 0.5), chest : (0.0, 0.1, -0.5, 0.5), lowerneck : (0.0, 0.1, -0.5, 0.5), upperneck : (0.0, 0.1, -0.5, 0.5), lclavicle : (0.0, 0.1, -0.5, 0.5), lshoulder : (0.0, 0.1, -0.5, 0.5), lelbow : (0.0, 0.1, -0.5, 0.5), lwrist : (0.0, 0.1, -0.5, 0.5), rclavicle : (0.0, 0.1, -0.5, 0.5), rshoulder : (0.0, 0.1, -0.5, 0.5), relbow : (0.0, 0.1, -0.5, 0.5), rwrist : (0.0, 0.1, -0.5, 0.5), } ''' mu, sigma, lower, upper ''' noise_vel = { root : (0.0, 0.1, -0.5, 0.5), lhip : (0.0, 0.1, -0.5, 0.5), lknee : (0.0, 0.1, -0.5, 0.5), lankle : (0.0, 0.1, -0.5, 0.5), rhip : (0.0, 0.1, -0.5, 0.5), rknee : (0.0, 0.1, -0.5, 0.5), rankle : (0.0, 0.1, -0.5, 0.5), lowerback : (0.0, 0.1, -0.5, 0.5), upperback : (0.0, 0.1, -0.5, 0.5), chest : (0.0, 0.1, -0.5, 0.5), lowerneck : (0.0, 0.1, -0.5, 0.5), upperneck : (0.0, 0.1, -0.5, 0.5), lclavicle : (0.0, 0.1, -0.5, 0.5), lshoulder : (0.0, 0.1, -0.5, 0.5), lelbow : (0.0, 0.1, -0.5, 0.5), lwrist : (0.0, 0.1, -0.5, 0.5), rclavicle : (0.0, 0.1, -0.5, 0.5), rshoulder : (0.0, 0.1, -0.5, 0.5), relbow : (0.0, 0.1, -0.5, 0.5), rwrist : (0.0, 0.1, -0.5, 0.5), } collison_ignore_pairs = [ ] friction_lateral = 0.8 friction_spinning = 0.3 restitution = 0.0 sum_joint_weight = 0.0 for key, val in joint_weight.items(): sum_joint_weight += val for key, val in joint_weight.items(): joint_weight[key] /= sum_joint_weight ================================================ FILE: bullet/__init__.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. ================================================ FILE: bullet/bullet_client.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. ''' This class was slightly edited from the code existing in one of examples in PyBullet. ''' import functools import inspect import pybullet class BulletClient(object): """A wrapper for pybullet to manage different clients.""" def __init__(self, connection_mode=pybullet.DIRECT, options=""): """Create a simulation and connect to it.""" self._client = pybullet.connect(pybullet.SHARED_MEMORY) if (self._client < 0): self._client = pybullet.connect(connection_mode, options=options) self._shapes = {} def __del__(self): """Clean up connection if not already done.""" try: pybullet.disconnect(physicsClientId=self._client) except pybullet.error: pass def __getattr__(self, name): """Inject the client id into Bullet functions.""" attribute = getattr(pybullet, name) if inspect.isbuiltin(attribute): attribute = functools.partial(attribute, physicsClientId=self._client) return attribute ================================================ FILE: bullet/bullet_render.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. from OpenGL.GL import * from OpenGL.GLUT import * from OpenGL.GLU import * import numpy as np import pybullet from fairmotion.viz import gl_render from fairmotion.ops import conversions from fairmotion.utils import constants def glTransform(quaternion): glMultMatrixd(T.transpose().ravel()) class RenderOptionBody(): def __init__(self): self.render = True self.render_edge = True self.render_face = True self.color_edge = [0.0, 0.0, 0.0, 1.0] self.color_face = [0.5, 0.5, 1.0, 0.5] self.line_width = 1.0 self.scale = 1.0 self.lighting = False class RenderOptionJoint(): def __init__(self): self.render = False self.render_pos = True self.render_ori = True self.color_pos = [0, 0, 0, 1.0] self.color_ori = [[1, 0, 0], [0, 1, 0], [0, 0, 1]] self.line_width = 1.0 self.scale = 1.0 self.lighting = False class RenderOption(): def __init__(self): self.body = RenderOptionBody() self.joint = RenderOptionJoint() def render_joint(joint, option): glPushAttrib(GL_LIGHTING) if option.lighting: glEnable(GL_LIGHTING) else: glDisable(GL_LIGHTING) j_type = joint.type() T0 = joint.child_bodynode.transform() T1 = joint.transform_from_child_body_node() T = T0.dot(T1) R, p = conversions.T2Rp(T) scale = option.scale if option.render_pos: gl_render.render_point(p, color=option.color_pos, scale=scale, radius=0.1) if option.render_ori: if j_type == "WeldJoint": pass elif j_type == "RevoluteJoint": axis1 = joint.axis_in_world_frame() gl_render.render_line(p, p + scale * axis1, color=option.color_ori[0]) elif j_type == "UniversalJoint": axis1 = joint.axis1_in_world_frame() axis2 = joint.axis2_in_world_frame() gl_render.render_line(p, p + scale * axis1, color=option.color_ori[0]) gl_render.render_line(p, p + scale * axis2, color=option.color_ori[1]) elif j_type == "EulerJoint": gl_render.render_transform(T, scale=scale, color_pos=option.color_pos, color_ori=option.color_ori) elif j_type == "BallJoint": gl_render.render_transform(T, scale=scale, color_pos=option.color_pos, color_ori=option.color_ori) elif j_type == "TranslationalJoint": gl_render.render_transform(T, scale=scale, color_pos=option.color_pos, color_ori=option.color_ori) elif j_type == "FreeJoint": gl_render.render_transform(T, scale=scale, color_pos=option.color_pos, color_ori=option.color_ori) else: raise NotImplementedError("Not Implemented") glPopAttrib(GL_LIGHTING) def get_wing_info(world, bid1, bid2, ntype): body1 = world.skel.body(bid1) body2 = world.skel.body(bid2) joint1 = body1.parent_joint joint2 = body2.parent_joint p1 = joint1.position_in_world_frame() c1 = body1.com() p1_end = p1 + 2 * (c1 - p1) p2 = joint2.position_in_world_frame() c2 = body2.com() p2_end = p2 + 2 * (c2 - p2) if ntype == 0: v1 = p1_end v2 = p2_end v3 = 0.5 * (p1 + p2) elif ntype == 1: v1 = p2_end v2 = p1_end v3 = 0.5 * (p1 + p2) elif ntype == 2: v1 = p2_end v2 = p1 v3 = 0.5 * (p1_end + p2) elif ntype == 3: v1 = p1 v2 = p2_end v3 = 0.5 * (p1_end + p2) else: raise NotImplementedError("Not Implemented") c = 0.333333 * (v1 + v2 + v3) n = np.cross(v1 - v3, v2 - v3) n = n / np.linalg.norm(n) return v1, v2, v3, c, n def render_wing_force_one_segment(wing_info, force, option): v1, v2, v3, c, n = wing_info if option.wing.render_force: gl_render.render_line(c, c + 0.01 * force, color=option.wing.color_force) def render_wing_one_segment(wing_info, option): v1, v2, v3, c, n = wing_info if option.wing.render_face: gl_render.render_tri(v3, v1, v2, color=option.wing.color_face) if option.wing.render_normal: gl_render.render_line(c, c + n, color=option.wing.color_normal) def render_wing(world, option): glPushAttrib(GL_LIGHTING) if option.wing.lighting: glEnable(GL_LIGHTING) else: glDisable(GL_LIGHTING) for bid1, bid2, ntype in world.aero_flesh: wing_info = get_wing_info(world, bid1, bid2, ntype) render_wing_one_segment(wing_info, option) for bid1, bid2, ntype, f in world.render_temp: wing_info = get_wing_info(world, bid1, bid2, ntype) render_wing_force_one_segment(wing_info, f, option) glPopAttrib(GL_LIGHTING) def get_visual_aspects(skel): vas = [] for body in skel.bodynodes: for shape in body.shapenodes: vas.append(shape.visual_aspect_rgba()) return vas def set_visual_aspect(skel, vas): cnt = 0 for body in skel.bodynodes: for shape in body.shapenodes: c = vas[cnt] shape.set_visual_aspect_rgba(c) cnt += 1 def get_visual_aspects_body(body): vas = [] for shape in body.shapenodes: vas.append(shape.visual_aspect_rgba()) return vas def set_visual_aspect_body(body, vas): cnt = 0 for shape in body.shapenodes: c = vas[cnt] shape.set_visual_aspect_rgba(c) cnt += 1 def render_body(body, option, global_coord=True): T = body.parent_bodynode.T if body.parent_bodynode else constants.eye_T() glPushAttrib(GL_LIGHTING) if option.lighting: glEnable(GL_LIGHTING) else: glDisable(GL_LIGHTING) if option.render_face: s = option.scale glPushMatrix() if global_coord: gl_render.glTransform(T) glScalef(s, s, s) body.render_with_color(option.color_face) glPopMatrix() if option.render_edge: glLineWidth(option.line_width) s = option.scale glPushMatrix() if global_coord: gl_render.glTransform(T) glScalef(s, s, s) glPolygonMode(GL_FRONT_AND_BACK, GL_LINE) body.render_with_color(option.color_edge) glPolygonMode(GL_FRONT_AND_BACK, GL_FILL) glPopMatrix() glPopAttrib(GL_LIGHTING) def render_skeleton(skel, option): if option is None: option = RenderOption() if option.body.render: # skel.render_with_color(option.body.color_face) for body in skel.bodynodes: render_body(body, option.body) if option.wing.render: render_wing(world, option) if option.joint.render: for joint in skel.joints: render_joint(joint, option.joint) def render_world(world, option=None): if option is None: option = RenderOption() for skel in world.skeletons: render_skeleton(skel, option) # def render_joint_with_body(joint): # T0 = joint.child_bodynode.transform() # T1 = joint.transform_from_child_body_node() # def render_body(body): # T0 = body.transform() # body.shapenodes[0] # def render_skeleton(skel): # for joint in skel.joints: # body = joint.child_bodynode # T0 = joint.child_bodynode.transform() # T1 = joint.transform_from_child_body_node() def render_geom_bounding_box(geom_type, geom_size, color=[0, 0, 0, 1], T=constants.EYE_T): if geom_type==pybullet.GEOM_SPHERE: size = [2*geom_size[0], 2*geom_size[0], 2*geom_size[0]] gl_render.render_cube(T, size=size, color=color, solid=False) elif geom_type==pybullet.GEOM_BOX: size = [geom_size[0], geom_size[1], geom_size[2]] gl_render.render_cube(T, size=size, color=color, solid=False) elif geom_type==pybullet.GEOM_CAPSULE: size = [2*geom_size[1], 2*geom_size[1], 2*geom_size[1]+geom_size[0]] gl_render.render_cube(T, size=size, color=color, solid=False) else: raise NotImplementedError() def render_geom(geom_type, geom_size, color=[0.5, 0.5, 0.5, 1.0], T=constants.EYE_T): if geom_type==pybullet.GEOM_SPHERE: gl_render.render_sphere(T, geom_size[0], color=color, slice1=16, slice2=16) elif geom_type==pybullet.GEOM_BOX: gl_render.render_cube(T, size=[geom_size[0], geom_size[1], geom_size[2]], color=color) elif geom_type==pybullet.GEOM_CAPSULE: gl_render.render_capsule(T, geom_size[0], geom_size[1], color=color, slice=16) elif geom_type==pybullet.GEOM_CYLINDER: gl_render.render_cylinder(T, geom_size[0], geom_size[1], color=color, slice=16) # elif geom_type==pybullet.GEOM_PLANE: # pass # elif geom_type==pybullet.GEOM_MESH: # pass else: raise NotImplementedError() def render_geom_info(geom_type, geom_size, scale=1.0, color=[0, 0, 0, 1], T=constants.EYE_T, line_width=1.0): glPushMatrix() glScalef(scale, scale, scale) if geom_type==pybullet.GEOM_SPHERE: gl_render.render_sphere_info(T, geom_size[0], line_width=line_width, slice=32, color=color) elif geom_type==pybullet.GEOM_BOX: gl_render.render_cube(T, size=[geom_size[0], geom_size[1], geom_size[2]], color=color, solid=False, line_width=line_width) elif geom_type==pybullet.GEOM_CAPSULE: gl_render.render_capsule_info(T, geom_size[0], geom_size[1], line_width=line_width, slice=32, color=color) elif geom_type==pybullet.GEOM_CYLINDER: gl_render.render_cylinder_info(T, geom_size[0], geom_size[1], line_width=line_width, slice=32, color=color) # elif geom_type==pybullet.GEOM_PLANE: # pass # elif geom_type==pybullet.GEOM_MESH: # pass else: raise NotImplementedError() glPopMatrix() def render_links(pb_client, model): for j in range(pb_client.getNumJoints(model)): link_state = pb_client.getLinkState(model, j) p, Q = np.array(link_state[0]), np.array(link_state[1]) T = conversions.Qp2T(Q, p) gl_render.render_point(p, radius=0.01, color=[0, 1, 0, 1]) def render_joints(pb_client, model): for j in range(pb_client.getNumJoints(model)): joint_info = pb_client.getJointInfo(model, j) joint_local_p, joint_local_Q, link_idx = joint_info[14], joint_info[15], joint_info[16] T_joint_local = conversions.Qp2T( np.array(joint_local_Q), np.array(joint_local_p)) if link_idx == -1: link_world_p, link_world_Q = pb_client.getBasePositionAndOrientation(model) else: link_info = pb_client.getLinkState(model, link_idx) link_world_p, link_world_Q = link_info[0], link_info[1] T_link_world = conversions.Qp2T( np.array(link_world_Q), np.array(link_world_p)) T_joint_world = np.dot(T_link_world, T_joint_local) # Render joint position glPushMatrix() gl_render.glTransform(T_joint_world) gl_render.render_point(np.zeros(3), radius=0.02, color=[0, 0, 0, 1]) # Render joint axis depending on joint types joint_type = joint_info[2] if joint_type == pb_client.JOINT_FIXED: pass elif joint_type == pb_client.JOINT_REVOLUTE: axis = joint_info[13] gl_render.render_line(np.zeros(3), axis, color=[1, 0, 0, 1], line_width=1.0) elif joint_type == pb_client.JOINT_SPHERICAL: gl_render.render_transform(constants.eye_T(), scale=0.2) else: raise NotImplementedError() glPopMatrix() def render_joint_geoms(pb_client, model, radius=0.025, color=[0.5, 0.5, 0.5, 1]): for j in range(pb_client.getNumJoints(model)): joint_info = pb_client.getJointInfo(model, j) joint_local_p, joint_local_Q, link_idx = joint_info[14], joint_info[15], joint_info[16] T_joint_local = conversions.Qp2T( np.array(joint_local_Q), np.array(joint_local_p)) if link_idx == -1: link_world_p, link_world_Q = pb_client.getBasePositionAndOrientation(model) else: link_info = pb_client.getLinkState(model, link_idx) link_world_p, link_world_Q = link_info[0], link_info[1] T_link_world = conversions.Qp2T( np.array(link_world_Q), np.array(link_world_p)) T_joint_world = np.dot(T_link_world, T_joint_local) # Render joint position glPushMatrix() gl_render.render_sphere(T_joint_world, radius, color=color, slice1=16, slice2=16) gl_render.render_sphere_info(T_joint_world, radius, line_width=4.0, slice=32) glPopMatrix() def render_model(pb_client, model, draw_link=True, draw_link_info=True, draw_joint=False, draw_joint_geom=True, ee_indices=None, color=None, link_info_scale=1.0, link_info_color=[0, 0, 0, 1], link_info_line_width=1.0, lighting=True, ): if draw_link or draw_link_info: data_visual = pb_client.getVisualShapeData(model) for d in data_visual: if lighting: glEnable(GL_LIGHTING) else: glDisable(GL_LIGHTING) link_id = d[1] if link_id == -1: p, Q = pb_client.getBasePositionAndOrientation(model) else: link_state = pb_client.getLinkState(model, link_id) p, Q = link_state[4], link_state[5] p, Q = np.array(p), np.array(Q) R = conversions.Q2R(Q) T_joint = conversions.Rp2T(R, p) T_visual_from_joint = conversions.Qp2T(np.array(d[6]),np.array(d[5])) glPushMatrix() gl_render.glTransform(T_joint) gl_render.glTransform(T_visual_from_joint) # if color is None: color = d[7] # alpha = 0.5 if draw_joint else color[3] if color is None: color = [d[7][0], d[7][1], d[7][2], d[7][3]] if draw_link: render_geom(geom_type=d[2], geom_size=d[3], color=color) if draw_link_info: render_geom_info(geom_type=d[2], geom_size=d[3], color=link_info_color, scale=link_info_scale, line_width=link_info_line_width) # if ee_indices is not None and link_id in ee_indices: # render_geom_bounding_box(geom_type=d[2], geom_size=d[3], color=[0, 0, 0, 1]) glPopMatrix() if draw_joint_geom: render_joint_geoms(pb_client, model) glDisable(GL_DEPTH_TEST) if draw_joint: render_joints(pb_client, model) render_links(pb_client, model) glEnable(GL_DEPTH_TEST) def render_contacts(pb_client, model, scale_h=0.0005, scale_r=0.01, color=[1.0,0.1,0.0,0.5]): data = pb_client.getContactPoints(model) for d in data: p, n, l = np.array(d[6]), np.array(d[7]), d[9] p1 = p p2 = p + n * l * scale_h gl_render.render_arrow(p1, p2, D=scale_r, color=color) ================================================ FILE: bullet/bullet_utils.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. import numpy as np from fairmotion.ops import quaternion ''' Bullet uses 'xyzw' order for the quaternion. quat_out_order lets the bullet_utils interprets the order for the application correctly. ''' xyzw_in = True def set_base_pQvw(pb_client, body_id, p, Q, v=None, w=None): ''' Set positions, orientations, linear and angular velocities of the base link. ''' if not xyzw_in: Q = quaternion.Q_op(Q, op=['change_order'], xyzw_in=False) pb_client.resetBasePositionAndOrientation(body_id, p, Q) if v is not None and w is not None: pb_client.resetBaseVelocity(body_id, v, w) def get_base_pQvw(pb_client, body_id): ''' Returns position, orientation, linear and angular velocities of the base link. ''' p, Q = pb_client.getBasePositionAndOrientation(body_id) p, Q = np.array(p), np.array(Q) if not xyzw_in: Q = quaternion.Q_op(Q, op=['change_order'], xyzw_in=True) v, w = pb_client.getBaseVelocity(body_id) v, w = np.array(v), np.array(w) return p, Q, v, w def get_link_pQvw(pb_client, body_id, indices=None): ''' Returns positions, orientations, linear and angular velocities given link indices. Please use get_base_pQvw for the base link. ''' if indices is None: indices = range(pb_client.getNumJoints(body_id)) num_indices = len(indices) assert num_indices > 0 ls = pb_client.getLinkStates(body_id, indices, computeLinkVelocity=True) ps = [np.array(ls[j][0]) for j in range(num_indices)] if not xyzw_in: Qs = [ quaternion.Q_op(np.array(ls[j][1]), op=['change_order'], xyzw_in=True) \ for j in range(num_indices)] else: Qs = [np.array(ls[j][1]) for j in range(num_indices)] vs = [np.array(ls[j][6]) for j in range(num_indices)] ws = [np.array(ls[j][7]) for j in range(num_indices)] if num_indices == 1: return ps[0], Qs[0], vs[0], ws[0] else: return ps, Qs, vs, ws def get_joint_torques(pb_client, body_id, indices=None): ''' This will return joint torques applied during the previous simulation ''' if indices is None: indices = range(pb_client.getNumJoints(body_id)) num_indices = len(indices) assert num_indices > 0 js = pb_client.getJointStatesMultiDof(body_id, indices) tqs = [np.array(js[j][3]) for j in range(num_indices)] if num_indices == 1: return tqs[0] else: return tqs def set_joint_pv(pb_client, body_id, indices, ps, vs): ''' Set positions and velocities given joint indices. Please note that the values are locally repsented w.r.t. its parent joint ''' ps_processed = ps.copy() for i in range(len(ps_processed)): if len(ps_processed[i]) == 4 and not xyzw_in: ps_processed[i] = \ quaternion.Q_op(ps_processed[i], op=['change_order'], xyzw_in=False) pb_client.resetJointStatesMultiDof(body_id, indices, ps_processed, vs) def get_joint_pv(pb_client, body_id, indices=None): ''' Return positions and velocities given joint indices. Please note that the values are locally repsented w.r.t. its parent joint ''' if indices is None: indices = range(pb_client.getNumJoints(body_id)) num_indices = len(indices) assert num_indices > 0 js = pb_client.getJointStatesMultiDof(body_id, indices) ps = [] vs = [] for j in range(num_indices): p = np.array(js[j][0]) v = np.array(js[j][1]) if len(p) == 4 and not xyzw_in: p = quaternion.Q_op(p, op=['change_order'], xyzw_in=True) ps.append(p) vs.append(v) if num_indices == 1: return ps[0], vs[0] else: return ps, vs def get_state_all(pb_client, body_id): ''' Return all state information of the given body. This includes pQvw of the base link and p and v for the all joints ''' p, Q, v, w = get_base_pQvw(pb_client, body_id) ps, vs = get_joint_pv(pb_client, body_id) return [p, Q, v, w, ps, vs] def set_state_all(pb_client, body_id, states): ''' Set all state information of the given body. States should include pQvw of the base link and p and v for the all joints ''' p, Q, v, w, ps, vs = states assert pb_client.getNumJoints(body_id) == len(ps) set_base_pQvw(pb_client, body_id, p, Q, v, w) indices = range(len(ps)) ''' Handling fixed joints ''' for i in indices: if len(ps[i])==0: ps[i] = [0] if len(vs[i])==0: vs[i] = [0] set_joint_pv(pb_client, body_id, indices, ps, vs) def get_mass(pb_client, body_id, indices=None): ''' Return masses of the links ''' if indices is None: indices = range(-1, pb_client.getNumJoints(body_id)) masses = [] for i in indices: di = pb_client.getDynamicsInfo(body_id, i) masses.append(di[0]) return masses def compute_com_and_com_vel(pb_client, body_id, indices=None): ''' Return the center-of-mass and the center-of-mass velocity ''' if indices is None: indices = range(-1, pb_client.getNumJoints(body_id)) total_mass = 0.0 com = np.zeros(3) com_vel = np.zeros(3) for i in indices: di = pb_client.getDynamicsInfo(body_id, i) mass = di[0] if i==-1: p, _, v, _ = get_base_pQvw(pb_client, body_id) else: ls = pb_client.getLinkState(body_id, i, computeLinkVelocity=True) p, v = np.array(ls[0]), np.array(ls[6]) total_mass += mass com += mass * p com_vel += mass * v com /= total_mass com_vel /= total_mass return com, com_vel def _compute_com_and_com_vel(pb_client, body_id, indices=None, masses=None): if indices is None: indices = range(-1, pb_client.getNumJoints(body_id)) if masses is None: masses = get_mass(pb_client, body_id, indices) assert len(indices)==len(masses) total_mass = 0.0 com = np.zeros(3) com_vel = np.zeros(3) indices_wo_base = [] masses_wo_base = [] for i in range(len(indices)): if indices[i]<0: p, _, v, _ = get_base_pQvw(pb_client, body_id) mass = masses[i] total_mass += mass com += mass * p com_vel = mass * v else: indices_wo_base.append(indices[i]) masses_wo_base.append(masses[i]) ls = pb_client.getLinkStates(body_id, indices_wo_base, computeLinkVelocity=True) for i in range(len(masses_wo_base)): mass = masses_wo_base[i] p, v = np.array(ls[i][0]), np.array(ls[i][6]) total_mass += mass com += mass * p com_vel = mass * v com /= total_mass com_vel /= total_mass return com, com_vel def compute_PD_forces(pb_client, body_id, joint_indices, desired_positions, desired_velocities, kps, kds, max_forces): ''' Compute PD forces given target values (P and D) and the simulated agent. This was implented because PD_CONTROL for setJointMotorControlMultiDofArray in PyBullet does not support spherical joint yet. ''' forces = [] js = pb_client.getJointStatesMultiDof(body_id, joint_indices) for i in range(len(joint_indices)): joint_pos = js[i][0] joint_vel = js[i][1] # print(i, desired_positions[i], joint_pos) if len(joint_pos) == 1: desired_pos = desired_positions[i] desired_vel = desired_velocities[i] qerror = desired_pos - joint_pos[0] qdoterror = desired_vel - joint_vel[0] elif len(joint_pos) == 4: desired_pos = desired_positions[i] desired_vel = desired_velocities[i] qerror = np.array(pb_client.getAxisDifferenceQuaternion(desired_pos, joint_pos)) qdoterror = np.array(desired_vel - joint_vel) else: raise NotImplementedError f = kps[i] * qerror + kds[i] * qdoterror f = np.clip(f, -max_forces[i], max_forces[i]) forces.append(f) return forces ================================================ FILE: data/character/amass.urdf ================================================ ================================================ FILE: data/motion/amass_hierarchy.bvh 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-1.566730 1.647890 1.151370 2.061972 19.228048 10.722253 0.946285 25.098790 8.257032 -28.659642 12.778994 -6.715104 -54.709594 6.106329 2.516392 6.220638 -24.263260 5.647287 -10.199232 -7.889790 -2.125972 22.593057 -6.737076 -3.036922 56.135575 -3.787290 8.299692 -15.718987 19.272917 -14.737392 2.535637 2.225128 -4.791788 0.944521 0.409875 1.012444 118.773853 0.467390 86.869849 2.128200 -1.441919 -6.484566 -3.222467 -2.211675 39.128196 -0.562150 9.998122 -15.388962 0.000000 -0.000000 0.000000 3.175313 -0.915331 -4.742258 -0.318568 -0.186882 17.149032 3.469850 -6.008457 -12.135271 0.000000 -0.000000 0.000000 2.015544 3.782101 15.079069 -3.736412 3.672299 -2.208273 -1.385970 1.591492 1.419716 2.557007 20.533361 9.541613 0.988074 25.617019 7.516366 -28.470490 12.598924 -7.151985 -53.521300 5.443445 3.105121 5.275845 -25.680554 6.410546 -9.566998 -7.943833 -2.741687 23.193537 -7.002428 -3.246379 56.146280 -4.889260 8.439087 -15.303474 15.753751 -13.894366 2.260492 1.785754 -1.569507 0.944521 0.409875 1.012444 118.773853 0.467390 86.869849 2.128200 -1.441919 -6.484566 -3.222467 -2.211675 39.128196 -0.562150 9.998122 -15.388962 0.000000 -0.000000 0.000000 3.175313 -0.915331 -4.742258 -0.318568 -0.186882 17.149032 3.469850 -6.008457 -12.135271 0.000000 -0.000000 0.000000 2.015544 3.782101 15.079069 -3.736412 3.672299 -2.208273 -1.385970 1.591492 1.419716 2.557007 20.533361 9.541613 0.988074 25.617019 7.516366 -28.470490 12.598924 -7.151985 -53.521300 5.443445 3.105121 5.275845 -25.680554 6.410546 -9.566998 -7.943833 -2.741687 23.193537 -7.002428 -3.246379 56.146280 -4.889260 8.439087 -15.303474 15.753751 -13.894366 2.260492 1.785754 -1.569507 ================================================ FILE: data/spec/spec_env_humanoid_imitation_expert0.yaml ================================================ run: PPO name: expert0 local_dir: /home/jungdam/Research/opensource/ScaDiver/data/learning/cmu/experts/ checkpoint_freq: 100 checkpoint_at_end: true stop: time_total_s: 720000 config: env: HumanoidImitation log_level: WARN gamma: 0.95 lambda: 0.95 clip_param: 0.2 kl_coeff: 0.0 vf_clip_param: 1000 num_sgd_iter: 20 lr: 0.00001 sgd_minibatch_size: 500 horizon: 900 train_batch_size: 50000 rollout_fragment_length: 100 framework: torch num_envs_per_worker: 1 num_cpus_per_worker: 1 num_gpus_per_worker: 0 model: custom_model: fcnn custom_model_config: log_std_type: constant sample_std: 0.1 num_workers: 8 num_gpus: 0 batch_mode: truncate_episodes observation_filter: NoFilter env_config: lazy_creation: true project_dir: /home/jungdam/Research/opensource/ScaDiver/ fps_sim: 480 fps_con: 30 self_collision: true add_noise: false verbose: false state: choices: ['body', 'task'] action: type: "absolute" # 'absolute', 'relative' range_min: -3.0 range_max: 3.0 range_min_pol: -3.0 range_max_pol: 3.0 reward: fn_def: default: name: total op: mul child_nodes: - name: pose_pos op: leaf weight: 1.0 kernel: type: gaussian scale: 40.0 - name: pose_vel op: leaf weight: 1.0 kernel: type: gaussian scale: 1.0 - name: ee op: leaf weight: 1.0 kernel: type: gaussian scale: 5.0 - name: root op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 - name: com op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 fn_map: - default early_term: choices: # 'sim_div', 'sim_window', task_end', 'falldown', 'low_reward' - task_end - low_reward low_reward_thres: 0.1 eoe_margin: 0.5 character: name: - agent1 char_info_module: - amass_char_info.py sim_char_file: - data/character/amass.urdf ref_motion_scale: - 1.0 base_motion_file: - data/motion/amass_hierarchy.bvh ref_motion_db: - data: dir: # We assume that all motions in the cluster0 are in this directory - data/motion/cmu/expert0/ actuation: - spd self_collision: - true environment_file: [] ================================================ FILE: data/spec/spec_env_humanoid_imitation_expert1.yaml ================================================ run: PPO name: expert1 local_dir: /home/jungdam/Research/opensource/ScaDiver/data/learning/cmu/experts/ checkpoint_freq: 100 checkpoint_at_end: true stop: time_total_s: 720000 config: env: HumanoidImitation log_level: WARN gamma: 0.95 lambda: 0.95 clip_param: 0.2 kl_coeff: 0.0 vf_clip_param: 1000 num_sgd_iter: 20 lr: 0.00001 sgd_minibatch_size: 500 horizon: 900 train_batch_size: 50000 rollout_fragment_length: 100 framework: torch num_envs_per_worker: 1 num_cpus_per_worker: 1 num_gpus_per_worker: 0 model: custom_model: fcnn custom_model_config: log_std_type: constant sample_std: 0.1 num_workers: 8 num_gpus: 0 batch_mode: truncate_episodes observation_filter: NoFilter env_config: lazy_creation: true project_dir: /home/jungdam/Research/opensource/ScaDiver/ fps_sim: 480 fps_con: 30 self_collision: true add_noise: false verbose: false state: choices: ['body', 'task'] action: type: "absolute" # 'absolute', 'relative' range_min: -3.0 range_max: 3.0 range_min_pol: -3.0 range_max_pol: 3.0 reward: fn_def: default: name: total op: mul child_nodes: - name: pose_pos op: leaf weight: 1.0 kernel: type: gaussian scale: 40.0 - name: pose_vel op: leaf weight: 1.0 kernel: type: gaussian scale: 1.0 - name: ee op: leaf weight: 1.0 kernel: type: gaussian scale: 5.0 - name: root op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 - name: com op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 fn_map: - default early_term: choices: # 'sim_div', 'sim_window', task_end', 'falldown', 'low_reward' - task_end - low_reward low_reward_thres: 0.1 eoe_margin: 0.5 character: name: - agent1 char_info_module: - amass_char_info.py sim_char_file: - data/character/amass.urdf ref_motion_scale: - 1.0 base_motion_file: - data/motion/amass_hierarchy.bvh ref_motion_db: - data: dir: # We assume that all motions in the cluster1 are in this directory - data/motion/cmu/expert1/ actuation: - spd self_collision: - true environment_file: [] ================================================ FILE: data/spec/spec_env_humanoid_imitation_expert2.yaml ================================================ run: PPO name: expert2 local_dir: /home/jungdam/Research/opensource/ScaDiver/data/learning/cmu/experts/ checkpoint_freq: 100 checkpoint_at_end: true stop: time_total_s: 720000 config: env: HumanoidImitation log_level: WARN gamma: 0.95 lambda: 0.95 clip_param: 0.2 kl_coeff: 0.0 vf_clip_param: 1000 num_sgd_iter: 20 lr: 0.00001 sgd_minibatch_size: 500 horizon: 900 train_batch_size: 50000 rollout_fragment_length: 100 framework: torch num_envs_per_worker: 1 num_cpus_per_worker: 1 num_gpus_per_worker: 0 model: custom_model: fcnn custom_model_config: log_std_type: constant sample_std: 0.1 num_workers: 8 num_gpus: 0 batch_mode: truncate_episodes observation_filter: NoFilter env_config: lazy_creation: true project_dir: /home/jungdam/Research/opensource/ScaDiver/ fps_sim: 480 fps_con: 30 self_collision: true add_noise: false verbose: false state: choices: ['body', 'task'] action: type: "absolute" # 'absolute', 'relative' range_min: -3.0 range_max: 3.0 range_min_pol: -3.0 range_max_pol: 3.0 reward: fn_def: default: name: total op: mul child_nodes: - name: pose_pos op: leaf weight: 1.0 kernel: type: gaussian scale: 40.0 - name: pose_vel op: leaf weight: 1.0 kernel: type: gaussian scale: 1.0 - name: ee op: leaf weight: 1.0 kernel: type: gaussian scale: 5.0 - name: root op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 - name: com op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 fn_map: - default early_term: choices: # 'sim_div', 'sim_window', task_end', 'falldown', 'low_reward' - task_end - low_reward low_reward_thres: 0.1 eoe_margin: 0.5 character: name: - agent1 char_info_module: - amass_char_info.py sim_char_file: - data/character/amass.urdf ref_motion_scale: - 1.0 base_motion_file: - data/motion/amass_hierarchy.bvh ref_motion_db: - data: dir: # We assume that all motions in the cluster2 are in this directory - data/motion/cmu/expert2/ actuation: - spd self_collision: - true environment_file: [] ================================================ FILE: data/spec/spec_env_humanoid_imitation_expert3.yaml ================================================ run: PPO name: expert3 local_dir: /home/jungdam/Research/opensource/ScaDiver/data/learning/cmu/experts/ checkpoint_freq: 100 checkpoint_at_end: true stop: time_total_s: 720000 config: env: HumanoidImitation log_level: WARN gamma: 0.95 lambda: 0.95 clip_param: 0.2 kl_coeff: 0.0 vf_clip_param: 1000 num_sgd_iter: 20 lr: 0.00001 sgd_minibatch_size: 500 horizon: 900 train_batch_size: 50000 rollout_fragment_length: 100 framework: torch num_envs_per_worker: 1 num_cpus_per_worker: 1 num_gpus_per_worker: 0 model: custom_model: fcnn custom_model_config: log_std_type: constant sample_std: 0.1 num_workers: 8 num_gpus: 0 batch_mode: truncate_episodes observation_filter: NoFilter env_config: lazy_creation: true project_dir: /home/jungdam/Research/opensource/ScaDiver/ fps_sim: 480 fps_con: 30 self_collision: true add_noise: false verbose: false state: choices: ['body', 'task'] action: type: "absolute" # 'absolute', 'relative' range_min: -3.0 range_max: 3.0 range_min_pol: -3.0 range_max_pol: 3.0 reward: fn_def: default: name: total op: mul child_nodes: - name: pose_pos op: leaf weight: 1.0 kernel: type: gaussian scale: 40.0 - name: pose_vel op: leaf weight: 1.0 kernel: type: gaussian scale: 1.0 - name: ee op: leaf weight: 1.0 kernel: type: gaussian scale: 5.0 - name: root op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 - name: com op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 fn_map: - default early_term: choices: # 'sim_div', 'sim_window', task_end', 'falldown', 'low_reward' - task_end - low_reward low_reward_thres: 0.1 eoe_margin: 0.5 character: name: - agent1 char_info_module: - amass_char_info.py sim_char_file: - data/character/amass.urdf ref_motion_scale: - 1.0 base_motion_file: - data/motion/amass_hierarchy.bvh ref_motion_db: - data: dir: # We assume that all motions in the cluster3 are in this directory - data/motion/cmu/expert3/ actuation: - spd self_collision: - true environment_file: [] ================================================ FILE: data/spec/spec_env_humanoid_imitation_expert4.yaml ================================================ run: PPO name: expert4 local_dir: /home/jungdam/Research/opensource/ScaDiver/data/learning/cmu/experts/ checkpoint_freq: 100 checkpoint_at_end: true stop: time_total_s: 720000 config: env: HumanoidImitation log_level: WARN gamma: 0.95 lambda: 0.95 clip_param: 0.2 kl_coeff: 0.0 vf_clip_param: 1000 num_sgd_iter: 20 lr: 0.00001 sgd_minibatch_size: 500 horizon: 900 train_batch_size: 50000 rollout_fragment_length: 100 framework: torch num_envs_per_worker: 1 num_cpus_per_worker: 1 num_gpus_per_worker: 0 model: custom_model: fcnn custom_model_config: log_std_type: constant sample_std: 0.1 num_workers: 8 num_gpus: 0 batch_mode: truncate_episodes observation_filter: NoFilter env_config: lazy_creation: true project_dir: /home/jungdam/Research/opensource/ScaDiver/ fps_sim: 480 fps_con: 30 self_collision: true add_noise: false verbose: false state: choices: ['body', 'task'] action: type: "absolute" # 'absolute', 'relative' range_min: -3.0 range_max: 3.0 range_min_pol: -3.0 range_max_pol: 3.0 reward: fn_def: default: name: total op: mul child_nodes: - name: pose_pos op: leaf weight: 1.0 kernel: type: gaussian scale: 40.0 - name: pose_vel op: leaf weight: 1.0 kernel: type: gaussian scale: 1.0 - name: ee op: leaf weight: 1.0 kernel: type: gaussian scale: 5.0 - name: root op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 - name: com op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 fn_map: - default early_term: choices: # 'sim_div', 'sim_window', task_end', 'falldown', 'low_reward' - task_end - low_reward low_reward_thres: 0.1 eoe_margin: 0.5 character: name: - agent1 char_info_module: - amass_char_info.py sim_char_file: - data/character/amass.urdf ref_motion_scale: - 1.0 base_motion_file: - data/motion/amass_hierarchy.bvh ref_motion_db: - data: dir: # We assume that all motions in the cluster4 are in this directory - data/motion/cmu/expert4/ actuation: - spd self_collision: - true environment_file: [] ================================================ FILE: data/spec/spec_env_humanoid_imitation_expert5.yaml ================================================ run: PPO name: expert5 local_dir: /home/jungdam/Research/opensource/ScaDiver/data/learning/cmu/experts/ checkpoint_freq: 100 checkpoint_at_end: true stop: time_total_s: 720000 config: env: HumanoidImitation log_level: WARN gamma: 0.95 lambda: 0.95 clip_param: 0.2 kl_coeff: 0.0 vf_clip_param: 1000 num_sgd_iter: 20 lr: 0.00001 sgd_minibatch_size: 500 horizon: 900 train_batch_size: 50000 rollout_fragment_length: 100 framework: torch num_envs_per_worker: 1 num_cpus_per_worker: 1 num_gpus_per_worker: 0 model: custom_model: fcnn custom_model_config: log_std_type: constant sample_std: 0.1 num_workers: 8 num_gpus: 0 batch_mode: truncate_episodes observation_filter: NoFilter env_config: lazy_creation: true project_dir: /home/jungdam/Research/opensource/ScaDiver/ fps_sim: 480 fps_con: 30 self_collision: true add_noise: false verbose: false state: choices: ['body', 'task'] action: type: "absolute" # 'absolute', 'relative' range_min: -3.0 range_max: 3.0 range_min_pol: -3.0 range_max_pol: 3.0 reward: fn_def: default: name: total op: mul child_nodes: - name: pose_pos op: leaf weight: 1.0 kernel: type: gaussian scale: 40.0 - name: pose_vel op: leaf weight: 1.0 kernel: type: gaussian scale: 1.0 - name: ee op: leaf weight: 1.0 kernel: type: gaussian scale: 5.0 - name: root op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 - name: com op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 fn_map: - default early_term: choices: # 'sim_div', 'sim_window', task_end', 'falldown', 'low_reward' - task_end - low_reward low_reward_thres: 0.1 eoe_margin: 0.5 character: name: - agent1 char_info_module: - amass_char_info.py sim_char_file: - data/character/amass.urdf ref_motion_scale: - 1.0 base_motion_file: - data/motion/amass_hierarchy.bvh ref_motion_db: - data: dir: # We assume that all motions in the cluster5 are in this directory - data/motion/cmu/expert5/ actuation: - spd self_collision: - true environment_file: [] ================================================ FILE: data/spec/spec_env_humanoid_imitation_expert6.yaml ================================================ run: PPO name: expert6 local_dir: /home/jungdam/Research/opensource/ScaDiver/data/learning/cmu/experts/ checkpoint_freq: 100 checkpoint_at_end: true stop: time_total_s: 720000 config: env: HumanoidImitation log_level: WARN gamma: 0.95 lambda: 0.95 clip_param: 0.2 kl_coeff: 0.0 vf_clip_param: 1000 num_sgd_iter: 20 lr: 0.00001 sgd_minibatch_size: 500 horizon: 900 train_batch_size: 50000 rollout_fragment_length: 100 framework: torch num_envs_per_worker: 1 num_cpus_per_worker: 1 num_gpus_per_worker: 0 model: custom_model: fcnn custom_model_config: log_std_type: constant sample_std: 0.1 num_workers: 8 num_gpus: 0 batch_mode: truncate_episodes observation_filter: NoFilter env_config: lazy_creation: true project_dir: /home/jungdam/Research/opensource/ScaDiver/ fps_sim: 480 fps_con: 30 self_collision: true add_noise: false verbose: false state: choices: ['body', 'task'] action: type: "absolute" # 'absolute', 'relative' range_min: -3.0 range_max: 3.0 range_min_pol: -3.0 range_max_pol: 3.0 reward: fn_def: default: name: total op: mul child_nodes: - name: pose_pos op: leaf weight: 1.0 kernel: type: gaussian scale: 40.0 - name: pose_vel op: leaf weight: 1.0 kernel: type: gaussian scale: 1.0 - name: ee op: leaf weight: 1.0 kernel: type: gaussian scale: 5.0 - name: root op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 - name: com op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 fn_map: - default early_term: choices: # 'sim_div', 'sim_window', task_end', 'falldown', 'low_reward' - task_end - low_reward low_reward_thres: 0.1 eoe_margin: 0.5 character: name: - agent1 char_info_module: - amass_char_info.py sim_char_file: - data/character/amass.urdf ref_motion_scale: - 1.0 base_motion_file: - data/motion/amass_hierarchy.bvh ref_motion_db: - data: dir: # We assume that all motions in the cluster6 are in this directory - data/motion/cmu/expert6/ actuation: - spd self_collision: - true environment_file: [] ================================================ FILE: data/spec/spec_env_humanoid_imitation_expert7.yaml ================================================ run: PPO name: expert7 local_dir: /home/jungdam/Research/opensource/ScaDiver/data/learning/cmu/experts/ checkpoint_freq: 100 checkpoint_at_end: true stop: time_total_s: 720000 config: env: HumanoidImitation log_level: WARN gamma: 0.95 lambda: 0.95 clip_param: 0.2 kl_coeff: 0.0 vf_clip_param: 1000 num_sgd_iter: 20 lr: 0.00001 sgd_minibatch_size: 500 horizon: 900 train_batch_size: 50000 rollout_fragment_length: 100 framework: torch num_envs_per_worker: 1 num_cpus_per_worker: 1 num_gpus_per_worker: 0 model: custom_model: fcnn custom_model_config: log_std_type: constant sample_std: 0.1 num_workers: 8 num_gpus: 0 batch_mode: truncate_episodes observation_filter: NoFilter env_config: lazy_creation: true project_dir: /home/jungdam/Research/opensource/ScaDiver/ fps_sim: 480 fps_con: 30 self_collision: true add_noise: false verbose: false state: choices: ['body', 'task'] action: type: "absolute" # 'absolute', 'relative' range_min: -3.0 range_max: 3.0 range_min_pol: -3.0 range_max_pol: 3.0 reward: fn_def: default: name: total op: mul child_nodes: - name: pose_pos op: leaf weight: 1.0 kernel: type: gaussian scale: 40.0 - name: pose_vel op: leaf weight: 1.0 kernel: type: gaussian scale: 1.0 - name: ee op: leaf weight: 1.0 kernel: type: gaussian scale: 5.0 - name: root op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 - name: com op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 fn_map: - default early_term: choices: # 'sim_div', 'sim_window', task_end', 'falldown', 'low_reward' - task_end - low_reward low_reward_thres: 0.1 eoe_margin: 0.5 character: name: - agent1 char_info_module: - amass_char_info.py sim_char_file: - data/character/amass.urdf ref_motion_scale: - 1.0 base_motion_file: - data/motion/amass_hierarchy.bvh ref_motion_db: - data: dir: # We assume that all motions in the cluster7 are in this directory - data/motion/cmu/expert7/ actuation: - spd self_collision: - true environment_file: [] ================================================ FILE: data/spec/spec_env_humanoid_imitation_moe.yaml ================================================ run: PPO name: moe local_dir: /home/jungdam/Research/opensource/ScaDiver/data/learning/cmu/moe/ checkpoint_freq: 100 checkpoint_at_end: true stop: time_total_s: 720000 config: env: HumanoidImitation log_level: WARN gamma: 0.95 lambda: 0.95 clip_param: 0.2 kl_coeff: 0.0 vf_clip_param: 1000 num_sgd_iter: 20 lr: 0.00001 sgd_minibatch_size: 500 horizon: 900 train_batch_size: 50000 rollout_fragment_length: 100 framework: torch num_envs_per_worker: 1 num_cpus_per_worker: 1 num_gpus_per_worker: 0 model: custom_model: moe_additive custom_model_config: log_std_type: constant sample_std: 0.1 expert_hiddens: - [128, 128] - [128, 128] - [128, 128] - [128, 128] - [128, 128] - [128, 128] - [128, 128] - [128, 128] expert_activations: - [relu, relu, linear] - [relu, relu, linear] - [relu, relu, linear] - [relu, relu, linear] - [relu, relu, linear] - [relu, relu, linear] - [relu, relu, linear] - [relu, relu, linear] expert_init_weights: - [1.0, 1.0, 0.01] - [1.0, 1.0, 0.01] - [1.0, 1.0, 0.01] - [1.0, 1.0, 0.01] - [1.0, 1.0, 0.01] - [1.0, 1.0, 0.01] - [1.0, 1.0, 0.01] - [1.0, 1.0, 0.01] expert_log_std_types: - constant - constant - constant - constant - constant - constant - constant - constant expert_sample_stds: - 0.1 - 0.1 - 0.1 - 0.1 - 0.1 - 0.1 - 0.1 - 0.1 expert_checkpoints: - False - False - False - False - False - False - False - False expert_learnable: - True - True - True - True - True - True - True - True num_workers: 8 num_gpus: 0 batch_mode: truncate_episodes observation_filter: NoFilter env_config: lazy_creation: true project_dir: /home/jungdam/Research/opensource/ScaDiver/ fps_sim: 480 fps_con: 30 self_collision: true add_noise: false verbose: false state: choices: ['body', 'task'] action: type: "absolute" # 'absolute', 'relative' range_min: -3.0 range_max: 3.0 range_min_pol: -3.0 range_max_pol: 3.0 reward: fn_def: default: name: total op: mul child_nodes: - name: pose_pos op: leaf weight: 1.0 kernel: type: gaussian scale: 40.0 - name: pose_vel op: leaf weight: 1.0 kernel: type: gaussian scale: 1.0 - name: ee op: leaf weight: 1.0 kernel: type: gaussian scale: 5.0 - name: root op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 - name: com op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 fn_map: - default early_term: choices: # 'sim_div', 'sim_window', task_end', 'falldown', 'low_reward' - task_end - low_reward low_reward_thres: 0.1 eoe_margin: 0.5 character: name: - agent1 char_info_module: - amass_char_info.py sim_char_file: - data/character/amass.urdf ref_motion_scale: - 1.0 base_motion_file: - data/motion/amass_hierarchy.bvh ref_motion_db: - data: dir: # We assume that all motions (cluster0 to cluster7) are in this directory - data/motion/cmu/moe/ actuation: - spd self_collision: - true environment_file: [] ================================================ FILE: data/spec/test_env_humanoid_imitation.yaml ================================================ run: PPO name: test local_dir: /home/jungdam/Research/opensource/ScaDiver/data/learning/test/ checkpoint_freq: 100 checkpoint_at_end: true stop: time_total_s: 720000 config: env: HumanoidImitation log_level: INFO gamma: 0.99 lambda: 0.95 clip_param: 0.2 kl_coeff: 0.0 vf_clip_param: 1000 num_sgd_iter: 20 lr: 0.00001 sgd_minibatch_size: 100 horizon: 900 train_batch_size: 1000 rollout_fragment_length: 100 framework: torch num_envs_per_worker: 1 num_cpus_per_worker: 1 num_gpus_per_worker: 0 model: custom_model: fcnn custom_model_config: log_std_type: constant sample_std: 0.2 num_workers: 8 num_gpus: 0 batch_mode: truncate_episodes observation_filter: NoFilter env_config: lazy_creation: false project_dir: /home/jungdam/Research/opensource/ScaDiver/ fps_sim: 480 fps_con: 30 add_noise: false verbose: false state: choices: ['body', 'task'] # 'body', 'imitation', 'interaction', 'task' action: type: "absolute" # 'absolute', 'relative' range_min: -3.0 range_max: 3.0 range_min_pol: -3.0 range_max_pol: 3.0 reward: fn_def: default: name: total op: mul child_nodes: - name: pose_pos op: leaf weight: 1.0 kernel: type: gaussian scale: 40.0 - name: pose_vel op: leaf weight: 1.0 kernel: type: gaussian scale: 1.0 - name: ee op: leaf weight: 1.0 kernel: type: gaussian scale: 5.0 - name: root op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 - name: com op: leaf weight: 1.0 kernel: type: gaussian scale: 2.5 fn_map: - default early_term: choices: # 'sim_div', 'sim_window', task_end', 'falldown', 'low_reward' - task_end - low_reward low_reward_thres: 0.1 eoe_margin: 0.5 character: name: - humanoid char_info_module: - amass_char_info.py sim_char_file: - data/character/amass.urdf ref_motion_scale: - 1.0 base_motion_file: - data/motion/amass_hierarchy.bvh ref_motion_db: - data: file: - data/motion/test.bvh actuation: - spd self_collision: - true environment_file: [] ================================================ FILE: env_humanoid_base.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. import os import numpy as np import copy from enum import Enum from collections import deque from fairmotion.ops import conversions from fairmotion.ops import math from fairmotion.ops import quaternion from fairmotion.core.motion import Pose from fairmotion.core.velocity import MotionWithVelocity from fairmotion.data import bvh import env_humanoid_tracking import sim_agent from abc import ABCMeta, abstractmethod class Env(metaclass=ABCMeta): class ActionMode(Enum): Absolute=0 # Use an absolute posture as an action Relative=1 # Use a relative posture from a reference posture as an action @classmethod def from_string(cls, string): if string=="absolute": return cls.Absolute if string=="relative": return cls.Relative raise NotImplementedError class StateChoice(Enum): Body=0 Task=1 @classmethod def from_string(cls, string): if string=="body": return cls.Body if string=="task": return cls.Task raise NotImplementedError class EarlyTermChoice(Enum): ''' Terminate when the simulation diverges ''' SimDiv=0 ''' Terminate when the given time elapses ''' SimWindow=1 ''' Terminate when the task completes or fails ''' TaskEnd=2 ''' Terminate when the agents falldown ''' Falldown=3 ''' Terminate when the average reward goes below a specified value ''' LowReward=4 @classmethod def from_string(cls, string): if string=="sim_div": return cls.SimDiv if string=="sim_window": return cls.SimWindow if string=="task_end": return cls.TaskEnd if string=="falldown": return cls.Falldown if string=="low_reward": return cls.LowReward raise NotImplementedError def __init__(self, config): project_dir = config['project_dir'] char_info_module = config['character'].get('char_info_module') sim_char_file = config['character'].get('sim_char_file') base_motion_file = config['character'].get('base_motion_file') ref_motion_scale = config['character'].get('ref_motion_scale') environment_file = config['character'].get('environment_file') ref_motion_file = config['character'].get('ref_motion_file') self_collision = config['character'].get('self_collision') actuation = config['character'].get('actuation') ''' Append project_dir to the given file path ''' if project_dir: for i in range(len(char_info_module)): char_info_module[i] = os.path.join(project_dir, char_info_module[i]) sim_char_file[i] = os.path.join(project_dir, sim_char_file[i]) base_motion_file[i] = os.path.join(project_dir, base_motion_file[i]) if environment_file is not None: for i in range(len(environment_file)): environment_file[i] = os.path.join(project_dir, environment_file[i]) ''' Create a base tracking environment ''' self._base_env = env_humanoid_tracking.Env( fps_sim=config['fps_sim'], fps_act=config['fps_con'], verbose=config['verbose'], char_info_module=char_info_module, sim_char_file=sim_char_file, ref_motion_scale=ref_motion_scale, self_collision=self_collision, contactable_body=config['early_term'].get('falldown_contactable_body'), actuation=actuation, ) self._pb_client = self._base_env._pb_client self._dt_con = 1.0/config['fps_con'] ''' Copy some of frequently used attributes from the base environemnt ''' self._num_agent = self._base_env._num_agent assert self._num_agent == len(base_motion_file) self._sim_agent = [self._base_env._agent[i] for i in range(self._num_agent)] self._v_up = self._base_env._v_up ''' State ''' self._state_choices = [Env.StateChoice.from_string(s) for s in config['state']['choices']] ''' Early Terminations ''' self._early_term_choices = [Env.EarlyTermChoice.from_string(s) for s in config['early_term']['choices']] self._reward_fn_def = config['reward']['fn_def'] self._reward_fn_map = config['reward']['fn_map'] self._reward_names = [self.get_reward_names( self._reward_fn_def[self._reward_fn_map[i]]) for i in range(self._num_agent)] ''' Check the existence of reward definitions, which are defined in our reward map ''' assert len(self._reward_fn_map) == self._num_agent for key in self._reward_fn_map: assert key in self._reward_fn_def.keys() self._verbose = config['verbose'] if Env.EarlyTermChoice.LowReward in self._early_term_choices: self._et_low_reward_thres = config['early_term']['low_reward_thres'] self._rew_queue = self._num_agent * [None] for i in range(self._num_agent): self._rew_queue[i] = deque(maxlen=int(1.0/self._dt_con)) ''' The environment automatically terminates after 'sim_window' seconds ''' if Env.EarlyTermChoice.SimWindow in self._early_term_choices: self._sim_window_time = config['early_term']['sim_window_time'] ''' The environment continues for "eoe_margin" seconds after end-of-episode is set by TRUE. This is useful for making the controller work for boundaries of reference motions ''' self._eoe_margin = config['early_term']['eoe_margin'] self._action_type = Env.ActionMode.from_string(config['action']['type']) ''' Base motion defines the initial posture (like t-pose) ''' self._base_motion = [] for i in range(self._num_agent): m = bvh.load(file=base_motion_file[i], motion=MotionWithVelocity(), scale=1.0, load_skel=True, load_motion=True, v_up_skel=self._sim_agent[i]._char_info.v_up, v_face_skel=self._sim_agent[i]._char_info.v_face, v_up_env=self._sim_agent[i]._char_info.v_up_env) m = MotionWithVelocity.from_motion(m) self._base_motion.append(m) ''' Create Kinematic Agents ''' self._kin_agent = [] for i in range(self._num_agent): self._kin_agent.append( sim_agent.SimAgent(pybullet_client=self._base_env._pb_client, model_file=sim_char_file[i], char_info=self._sim_agent[i]._char_info, ref_scale=ref_motion_scale[i], self_collision=self_collision[i], kinematic_only=True, verbose=config['verbose'])) ''' Define the action space of this environment. Here I used a 'normalizer' where 'real' values correspond to joint angles, and 'norm' values correspond to the output value of NN policy. The reason why it is used is that NN policy somtimes could output values that are within much larger or narrow range than we need for the environment. For example, if we apply tanh activation function at the last layer of NN, the output are always within (-1, 1), but we need bigger values for joint angles because 1 corresponds only to 57.3 degree. ''' self._action_space = [] for i in range(self._num_agent): dim = self._sim_agent[i].get_num_dofs() normalizer = math.Normalizer( real_val_max=config['action']['range_max']*np.ones(dim), real_val_min=config['action']['range_min']*np.ones(dim), norm_val_max=config['action']['range_max_pol']*np.ones(dim), norm_val_min=config['action']['range_min_pol']*np.ones(dim), apply_clamp=True) self._action_space.append(normalizer) self._com_vel = self._num_agent * [None] for i in range(self._num_agent): self._com_vel[i] = deque(maxlen=int(1.0/self._dt_con)) ''' Any necessary information needed for training this environment. This can be set by calling "set_learning_info". ''' self._learning_info = {} self.add_noise = config['add_noise'] def action_range(self, idx): return self._action_space[idx].real_val_min, self._action_space[idx].real_val_max def dim_action(self, idx): return self._action_space[idx].dim def dim_state(self, idx): return len(self.state(idx)) def dim_state_body(self, idx): return len(self.state_body(idx)) def dim_state_task(self, idx): return len(self.state_task(idx)) def set_learning_info(self, info): self._learning_info = info def update_learning_info(self, info): self._learning_info.update(info) def agent_avg_position(self, agents=None): if agents is None: agents=self._sim_agent return np.mean([(agent.get_root_state())[0] for agent in agents], axis=0) def agent_ave_facing_position(self, agents=None): if agents is None: agents=self._sim_agent return np.mean([agent.get_facing_position(self.get_ground_height()) for agent in agents], axis=0) def throw_obstacle(self): size = np.random.uniform(0.1, 0.3, 3) p = self.agent_avg_position() self._base_env.throw_obstacle(size, p) def split_action(self, action): assert len(action)%self._num_agent == 0 dim_action = len(action)//self._num_agent actions = [] idx = 0 for i in range(self._num_agent): actions.append(action[idx:idx+dim_action]) idx += dim_action return actions def compute_target_pose(self, idx, action): agent = self._sim_agent[idx] char_info = agent._char_info ''' the current posture should be deepcopied because action will modify it ''' if self._action_type == Env.ActionMode.Relative: ref_pose = copy.deepcopy(self.get_current_pose_from_motion(idx)) else: ref_pose = copy.deepcopy(self._base_motion[idx].get_pose_by_frame(0)) a_real = self._action_space[idx].norm_to_real(action) dof_cnt = 0 for j in agent._joint_indices: joint_type = agent.get_joint_type(j) ''' Fixed joint will not be affected ''' if joint_type == self._pb_client.JOINT_FIXED: continue ''' If the joint do not have correspondance, use the reference posture itself''' if char_info.bvh_map[j] == None: continue if self._action_type == Env.ActionMode.Relative: T = ref_pose.get_transform(char_info.bvh_map[j], local=True) elif self._action_type == Env.ActionMode.Absolute: T = ref_pose.skel.get_joint(char_info.bvh_map[j]).xform_from_parent_joint else: raise NotImplementedError R, p = conversions.T2Rp(T) if joint_type == self._pb_client.JOINT_SPHERICAL: dR = conversions.A2R(a_real[dof_cnt:dof_cnt+3]) dof_cnt += 3 elif joint_type == self._pb_client.JOINT_REVOLUTE: axis = agent.get_joint_axis(j) angle = a_real[dof_cnt:dof_cnt+1] dR = conversions.A2R(axis*angle) dof_cnt += 1 else: raise NotImplementedError T_new = conversions.Rp2T(np.dot(R, dR), p) ref_pose.set_transform(char_info.bvh_map[j], T_new, do_ortho_norm=False, local=True) return ref_pose def compute_init_pose_vel(self, add_noise): ''' This compute initial poses and velocities for all agents. The returned poses and velocites will be the initial pose and velocities of the simulated agent. ''' init_poses, init_vels = [], [] for i in range(self._num_agent): cur_pose = self._base_motion[i].get_pose_by_frame(0) cur_vel = self._base_motion[i].get_velocity_by_frame(0) if add_noise: cur_pose, cur_vel = self._base_env.add_noise_to_pose_vel( self._sim_agent[i], cur_pose, cur_vel) init_poses.append(cur_pose) init_vels.append(cur_vel) return init_poses, init_vels def callback_reset_prev(self, info): ''' This is called right before the main reset fn. is called. ''' return def callback_reset_after(self, info): ''' This is called right after the main reset fn. is called. ''' return def reset(self, info): self.callback_reset_prev(info) self._target_pose = [None for i in range(self._num_agent)] self._init_poses, self._init_vels = self.compute_init_pose_vel(info) self._base_env.reset(time=0.0, poses=self._init_poses, vels=self._init_vels) self._end_of_episode = False self._end_of_episode_reason = [] self._end_of_episode_intermediate = False self._end_of_episode_reason_intermediate = [] self._time_elapsed_after_end_of_episode = 0.0 for i in range(self._num_agent): self._com_vel[i].clear() self._com_vel[i].append(self._sim_agent[i].get_com_and_com_vel()[1]) if Env.EarlyTermChoice.LowReward in self._early_term_choices: for i in range(self._num_agent): self._rew_queue[i].clear() for j in range(self._rew_queue[i].maxlen): self._rew_queue[i].append(self.reward_max()) self.callback_reset_after(info) def callback_step_prev(self): return def callback_step_after(self): return def print_log_in_step(self): if self._verbose and self._end_of_episode: print('=================EOE=================') print('Reason:', self._end_of_episode_reason) print('TIME: (elapsed:%02f) (time_after_eoe: %02f)'\ %(self.get_elapsed_time(), self._time_elapsed_after_end_of_episode)) print('=====================================') def step(self, action): self.callback_step_prev() ''' Collect data for reward computation before the current step''' rew_data_prev = [self.reward_data(i) for i in range(self._num_agent)] assert len(action) == self._num_agent for i in range(self._num_agent): if isinstance(action[i], Pose): self._target_pose[i] = action[i] elif isinstance(action[i], np.ndarray): self._target_pose[i] = self.compute_target_pose(i, action[i]) else: print(type(action[i])) raise NotImplementedError for i in range(self._num_agent): self._com_vel[i].append(self._sim_agent[i].get_com_and_com_vel()[1]) ''' Update simulation ''' self._base_env.step(self._target_pose) self.callback_step_after() ''' Collect data for reward computation after the current step''' rew_data_next = [self.reward_data(i) for i in range(self._num_agent)] ''' Check conditions for end-of-episode. If 'eoe_margin' is larger than zero, the environment will continue for some time. ''' if not self._end_of_episode_intermediate: eoe_reason = [] for i in range(self._num_agent): eoe_reason += self.inspect_end_of_episode_per_agent(i) if Env.EarlyTermChoice.TaskEnd in self._early_term_choices: eoe_reason += self.inspect_end_of_episode_task() self._end_of_episode_intermediate = len(eoe_reason) > 0 self._end_of_episode_reason_intermediate = eoe_reason if self._end_of_episode_intermediate: self._time_elapsed_after_end_of_episode += self._dt_con if self._time_elapsed_after_end_of_episode >= self._eoe_margin: self._end_of_episode = True self._end_of_episode_reason = self._end_of_episode_reason_intermediate ''' Compute rewards ''' rews, infos = [], [] for i in range(self._num_agent): r, rd = self.reward(i, rew_data_prev, rew_data_prev, action) rews.append(r) info = { 'eoe_reason': self._end_of_episode_reason, 'rew_info': rd, 'learning_info': self._learning_info } infos.append(info) if Env.EarlyTermChoice.LowReward in self._early_term_choices: self._rew_queue[i].append(r) self.print_log_in_step() return rews, infos def state(self, idx): state = [] if Env.StateChoice.Body in self._state_choices: state.append(self.state_body(idx)) if Env.StateChoice.Task in self._state_choices: state.append(self.state_task(idx)) return np.hstack(state) @abstractmethod def state_body(self, idx): ''' This returns proprioceptive state of an agent as a numpy array ''' raise NotImplementedError def _state_body(self, agent, T_ref=None, include_com=True, include_p=True, include_Q=True, include_v=True, include_w=True, return_stacked=True): if T_ref is None: T_ref = agent.get_facing_transform(self.get_ground_height()) R_ref, p_ref = conversions.T2Rp(T_ref) R_ref_inv = R_ref.transpose() link_states = [] link_states.append(agent.get_root_state()) ps, Qs, vs, ws = agent.get_link_states() for j in agent._joint_indices: link_states.append((ps[j], Qs[j], vs[j], ws[j])) state = [] for i, s in enumerate(link_states): p, Q, v, w = s[0], s[1], s[2], s[3] if include_p: p_rel = np.dot(R_ref_inv, p - p_ref) state.append(p_rel) # relative position w.r.t. the reference frame if include_Q: Q_rel = conversions.R2Q(np.dot(R_ref_inv, conversions.Q2R(Q))) Q_rel = quaternion.Q_op(Q_rel, op=["normalize", "halfspace"]) state.append(Q_rel) # relative rotation w.r.t. the reference frame if include_v: v_rel = np.dot(R_ref_inv, v) state.append(v_rel) # relative linear vel w.r.t. the reference frame if include_w: w_rel = np.dot(R_ref_inv, w) state.append(w_rel) # relative angular vel w.r.t. the reference frame if include_com: if i==0: p_com = agent._link_masses[i] * p v_com = agent._link_masses[i] * v else: p_com += agent._link_masses[i] * p v_com += agent._link_masses[i] * v if include_com: p_com /= agent._link_total_mass v_com /= agent._link_total_mass state.append(np.dot(R_ref_inv, p_com - p_ref)) state.append(np.dot(R_ref_inv, v_com)) if return_stacked: return np.hstack(state) else: return state @abstractmethod def state_task(self, idx): ''' This returns a task-specifit state (numpy array) ''' raise NotImplementedError @abstractmethod def reward_data(self, idx): ''' This returns a dictionary that includes data to compute reward value ''' raise NotImplementedError @abstractmethod def reward_max(self): ''' This returns a maximum reward value ''' raise NotImplementedError @abstractmethod def reward_min(self): ''' This returns a minimum reward value ''' raise NotImplementedError def return_max(self, gamma): ''' This returns a maximum return (sum of rewards) ''' assert gamma < 1.0 return self.reward_max() / (1.0 - gamma) def return_min(self, gamma): ''' This returns a minimum return (sum of rewards) ''' assert gamma < 1.0 return self.reward_min() / (1.0 - gamma) @abstractmethod def get_task_error(self, idx, data_prev, data_next, action): ''' This computes a task-specific error and returns a dictionary that includes those errors ''' raise NotImplementedError def reward(self, idx, data_prev, data_next, action): ''' This returns a reward, and a dictionary ''' error = self.get_task_error(idx, data_prev, data_next, action) rew_fn_def = self._reward_fn_def[self._reward_fn_map[idx]] rew, rew_info = self.compute_reward(error, rew_fn_def) return rew, rew_info def get_reward_names(self, fn_def): rew_names = set() op = fn_def['op'] if op in ['add', 'mul']: for child in fn_def['child_nodes']: rew_names = rew_names.union(self.get_reward_names(child)) elif op == 'leaf': rew_names.add(fn_def['name']) else: raise NotImplementedError return rew_names def pretty_print_rew_info(self, rew_info, prefix=str()): print("%s > name: %s"%(prefix, rew_info['name'])) print("%s value: %s"%(prefix, rew_info['value'])) print("%s weight: %s"%(prefix, rew_info['weight'])) print("%s op: %s"%(prefix, rew_info['op'])) for child in rew_info["child_nodes"]: self.pretty_print_rew_info(child, prefix+"\t") def compute_reward(self, error, fn_def): ''' This computes a reward by using task-specific errors and the reward definition tree ''' op = fn_def['op'] n = fn_def['name'] if 'name' in fn_def.keys() else 'noname' w = fn_def['weight'] if 'weight' in fn_def.keys() else 1.0 rew_info = {'name': n, 'value': 0.0, 'op': op, 'weight': w, 'child_nodes': []} if op in ['add', 'sum']: rew = 0.0 for child in fn_def['child_nodes']: r, rd = self.compute_reward(error, child) rew += r rew_info['child_nodes'].append(rd) elif op in ['mul', 'multiply']: rew = 1.0 for child in fn_def['child_nodes']: r, rd = self.compute_reward(error, child) rew *= r rew_info['child_nodes'].append(rd) elif op == 'leaf': if 'kernel' in fn_def.keys(): kernel = fn_def['kernel'] else: kernel = None if 'weight_schedule' in fn_def.keys(): timesteps_total = self._learning_info['timesteps_total'] w *= math.lerp_from_paired_list( timesteps_total, fn_def['weight_schedule']) if kernel is None or kernel['type'] == "none": e = error[n] elif kernel['type'] == "gaussian": e = np.exp(-kernel['scale']*error[n]) else: raise NotImplementedError rew = w*e else: raise NotImplementedError rew_info['value'] = rew return rew, rew_info @abstractmethod def inspect_end_of_episode_task(self): ''' This checks whether task-specific END-OF-EPISODE events happen and returns a list that includes reasons ''' raise NotImplementedError def inspect_end_of_episode_per_agent(self, idx): eoe_reason = [] name = self._sim_agent[idx].get_name() if Env.EarlyTermChoice.Falldown in self._early_term_choices: check = self._base_env.check_falldown(self._sim_agent[idx]) if check: eoe_reason.append('[%s] falldown'%name) if Env.EarlyTermChoice.SimDiv in self._early_term_choices: check = self._base_env.is_sim_div(self._sim_agent[idx]) if check: eoe_reason.append('[%s] sim_div'%name) if Env.EarlyTermChoice.SimWindow in self._early_term_choices: check = self.get_elapsed_time() > self._sim_window_time if check: eoe_reason.append('[%s] sim_window'%name) if Env.EarlyTermChoice.LowReward in self._early_term_choices: check = np.mean(list(self._rew_queue[idx])) < self._et_low_reward_thres * self.reward_max() if check: eoe_reason.append('[%s] low_rewards'%name) return eoe_reason @abstractmethod def get_ground_height(self): ''' This returns height of the ground ''' raise NotImplementedError def get_elapsed_time(self): ''' This returns the elpased time after the environment was reset ''' return self._base_env._elapsed_time def set_elapsed_time(self, time): self._base_env._elapsed_time = time def render(self, rm): colors = rm.COLORS_FOR_AGENTS rm.gl.glEnable(rm.gl.GL_LIGHTING) rm.gl.glEnable(rm.gl.GL_BLEND) rm.gl.glBlendFunc(rm.gl.GL_SRC_ALPHA, rm.gl.GL_ONE_MINUS_SRC_ALPHA) self._base_env.render(rm, ground_height=self.get_ground_height()) if rm.flag['target_pose']: for i in range(self._num_agent): if self._target_pose[i] is None: continue agent = self._kin_agent[i] agent_state = agent.save_states() agent.set_pose(self._target_pose[i]) rm.gl.glPushAttrib(rm.gl.GL_LIGHTING|rm.gl.GL_DEPTH_TEST|rm.gl.GL_BLEND) rm.bullet_render.render_model(self._pb_client, agent._body_id, draw_link=True, draw_link_info=False, draw_joint=rm.flag['joint'], draw_joint_geom=False, ee_indices=agent._char_info.end_effector_indices, color=[colors[i][0], colors[i][1], colors[i][2], 0.5]) rm.gl.glPopAttrib() agent.restore_states(agent_state) if rm.flag['kin_model']: for i in range(self._num_agent): agent = self._kin_agent[i] rm.gl.glPushAttrib(rm.gl.GL_LIGHTING|rm.gl.GL_DEPTH_TEST|rm.gl.GL_BLEND) rm.bullet_render.render_model(self._pb_client, agent._body_id, draw_link=True, draw_link_info=False, draw_joint=rm.flag['joint'], draw_joint_geom=False, ee_indices=agent._char_info.end_effector_indices, color=[colors[i][0], colors[i][1], colors[i][2], 0.5]) if rm.flag['com_vel']: p, Q, v, w = agent.get_root_state() p, v = agent.get_com_and_com_vel() rm.gl_render.render_arrow(p, p+v, D=0.01, color=[0.5, 0.5, 0.5, 1]) rm.gl.glPopAttrib() ================================================ FILE: env_humanoid_imitation.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. import os import numpy as np import pickle import gzip import re import random from fairmotion.utils import utils from fairmotion.ops import conversions from fairmotion.core.motion import Motion from fairmotion.core.velocity import MotionWithVelocity from fairmotion.data import bvh import env_humanoid_base def load_motions(motion_files, skel, char_info, verbose): assert motion_files is not None motion_file_names = [] for names in motion_files: head, tail = os.path.split(names) motion_file_names.append(tail) if isinstance(motion_files[0], str): motion_dict = {} motion_all = [] for i, file in enumerate(motion_files): ''' If the same file is already loaded, do not load again for efficiency''' if file in motion_dict: m = motion_dict[file] else: if file.endswith('bvh'): m = bvh.load(motion=Motion(name=file, skel=skel), file=file, scale=1.0, load_skel=False, v_up_skel=char_info.v_up, v_face_skel=char_info.v_face, v_up_env=char_info.v_up_env) m = MotionWithVelocity.from_motion(m) elif file.endswith('bin'): m = pickle.load(open(file, "rb")) elif file.endswith('gzip') or file.endswith('gz'): with gzip.open(file, "rb") as f: m = pickle.load(f) else: raise Exception('Unknown Motion File Type') if verbose: print('Loaded: %s'%file) motion_all.append(m) elif isinstance(motion_files[0], MotionWithVelocity): motion_all = motion_files else: raise Exception('Unknown Type for Reference Motion') return motion_all, motion_file_names class Env(env_humanoid_base.Env): def __init__(self, config): super().__init__(config) self._initialized = False self._config = config self._ref_motion = None self._imit_window = [0.05, 0.15] self._start_time = 0.0 if config.get('lazy_creation'): if self._verbose: print('The environment was created in a lazy fashion.') print('The function \"create\" should be called before it') return self.create() def create(self): project_dir = self._config['project_dir'] ref_motion_db = self._config['character'].get('ref_motion_db') ref_motion_scale = self._config['character'].get('ref_motion_scale') ref_motion_file = [] for i, mdb in enumerate(ref_motion_db): motions = [] if mdb.get('cluster_info'): ''' Read reference motions based on the cluster labels ''' assert mdb.get('data') is None, \ 'This should not be specified when cluster_info is used' dir = mdb['cluster_info'].get('dir') label_file = mdb['cluster_info'].get('label_file') sample_id = mdb['cluster_info'].get('sample_id') labels = {} assert label_file if project_dir: label_file = os.path.join(project_dir, label_file) with open(label_file, 'r') as file: for line in file: l = re.split('[\t|\n|,|:| ]+', line) id, rank, score, filename = int(l[0]), int(l[1]), float(l[2]), str(l[3]) if id not in labels.keys(): labels[id] = [] labels[id].append({'rank': rank, 'socre': score, 'filename': filename}) num_cluster = len(labels.keys()) for j in range(num_cluster): if sample_id and j!=sample_id: continue for label in labels[j]: if project_dir: file = os.path.join(project_dir, dir, label['filename']) motions.append(file) else: ''' Read reference motions from the specified list of files and dirs ''' ref_motion_data = mdb.get('data') motions = [] if ref_motion_data.get('file'): motions += ref_motion_data.get('file') if ref_motion_data.get('dir'): for d in ref_motion_data.get('dir'): if project_dir: d = os.path.join(project_dir, d) motions += utils.files_in_dir(d, ext=".bvh", sort=True) if project_dir: for j in range(len(motions)): motions[j] = os.path.join(project_dir, motions[j]) ''' If num_sample is specified, we use only num_sample motions from the entire reference motions. 'random' chooses randomly, 'top' chooses the first num_sample ''' num_sample = mdb.get('num_sample') if num_sample: sample_method = mdb.get('sample_method') if sample_method == 'random': motions = random.choices(motions, k=num_sample) elif sample_method == 'top': motions = motions[:num_sample] else: raise NotImplementedError ref_motion_file.append(motions) ''' Load Reference Motion ''' self._ref_motion_all = [] self._ref_motion_file_names = [] for i in range(self._num_agent): ref_motion_all, ref_motion_file_names = \ load_motions(ref_motion_file[i], self._base_motion[i].skel, self._sim_agent[i]._char_info, self._verbose) self._ref_motion_all.append(ref_motion_all) self._ref_motion_file_names.append(ref_motion_file_names) ''' Should call reset after all setups are done ''' self.reset({'add_noise': False}) self._initialized = True if self._verbose: print('----- Humanoid Imitation Environment Created -----') for i in range(self._num_agent): print('[Agent%d]: state(%d) and action(%d)' \ %(i, len(self.state(i)), self._action_space[i].dim)) print('-------------------------------') def callback_reset_prev(self, info): ''' Choose a reference motion randomly whenever reset ''' self._ref_motion = self.sample_ref_motion() ''' Choose a start time for the current reference motion ''' start_time = info.get('start_time') if start_time is not None: self._start_time = start_time else: self._start_time = \ np.random.uniform(0.0, self._ref_motion[0].length()) def callback_reset_after(self, info): for i in range(self._num_agent): self._kin_agent[i].set_pose( self._init_poses[i], self._init_vels[i]) def callback_step_after(self): ''' This is necessary to compute the reward correctly ''' cur_time = self.get_current_time() for i in range(self._num_agent): self._kin_agent[i].set_pose( self._ref_motion[i].get_pose_by_time(cur_time), self._ref_motion[i].get_velocity_by_time(cur_time)) def print_log_in_step(self): if self._verbose and self._end_of_episode: print('=================EOE=================') print('Reason:', self._end_of_episode_reason) print('TIME: (start:%02f) (elapsed:%02f) (time_after_eoe: %02f)'\ %(self._start_time, self.get_elapsed_time(), self._time_elapsed_after_end_of_episode)) print('=====================================') def compute_init_pose_vel(self, info): ''' This performs reference-state-initialization (RSI) ''' init_poses, init_vels = [], [] cur_time = self.get_current_time() for i in range(self._num_agent): ''' Set the state of simulated agent by using the state of reference motion ''' cur_pose = self._ref_motion[i].get_pose_by_time(cur_time) cur_vel = self._ref_motion[i].get_velocity_by_time(cur_time) ''' Add noise to the state if necessary ''' if info.get('add_noise'): cur_pose, cur_vel = \ self._base_env.add_noise_to_pose_vel( self._sim_agent[i], cur_pose, cur_vel) init_poses.append(cur_pose) init_vels.append(cur_vel) return init_poses, init_vels def state_body(self, idx): return self._state_body(self._sim_agent[idx], T_ref=None, include_com=True, include_p=True, include_Q=True, include_v=True, include_w=True, return_stacked=True) def state_task(self, idx): state = [] poses, vels = [], [] if self._ref_motion is not None: ref_motion = self._ref_motion[idx] else: ref_motion = self._base_motion[idx] for dt in self._imit_window: t = np.clip( self.get_current_time() + dt, 0.0, ref_motion.length()) poses.append(ref_motion.get_pose_by_time(t)) vels.append(ref_motion.get_velocity_by_time(t)) state.append(self.state_imitation(self._sim_agent[idx], self._kin_agent[idx], poses, vels, include_abs=True, include_rel=True)) return np.hstack(state) def state_imitation(self, sim_agent, kin_agent, poses, vels, include_abs, include_rel): assert len(poses) == len(vels) R_sim, p_sim = conversions.T2Rp( sim_agent.get_facing_transform(self.get_ground_height())) R_sim_inv = R_sim.transpose() state_sim = self._state_body(sim_agent, None, return_stacked=False) state = [] state_kin_orig = kin_agent.save_states() for pose, vel in zip(poses, vels): kin_agent.set_pose(pose, vel) state_kin = self._state_body(kin_agent, None, return_stacked=False) # Add pos/vel values if include_abs: state.append(np.hstack(state_kin)) # Add difference of pos/vel values if include_rel: for j in range(len(state_sim)): if len(state_sim[j])==3: state.append(state_sim[j]-state_kin[j]) elif len(state_sim[j])==4: state.append( self._pb_client.getDifferenceQuaternion(state_sim[j], state_kin[j])) else: raise NotImplementedError ''' Add facing frame differences ''' R_kin, p_kin = conversions.T2Rp( kin_agent.get_facing_transform(self.get_ground_height())) state.append(np.dot(R_sim_inv, p_kin - p_sim)) state.append(np.dot(R_sim_inv, kin_agent.get_facing_direction())) kin_agent.restore_states(state_kin_orig) return np.hstack(state) def reward_data(self, idx): data = {} data['sim_root_pQvw'] = self._sim_agent[idx].get_root_state() data['sim_link_pQvw'] = self._sim_agent[idx].get_link_states() data['sim_joint_pv'] = self._sim_agent[idx].get_joint_states() data['sim_facing_frame'] = self._sim_agent[idx].get_facing_transform(self.get_ground_height()) data['sim_com'], data['sim_com_vel'] = self._sim_agent[idx].get_com_and_com_vel() data['kin_root_pQvw'] = self._kin_agent[idx].get_root_state() data['kin_link_pQvw'] = self._kin_agent[idx].get_link_states() data['kin_joint_pv'] = self._kin_agent[idx].get_joint_states() data['kin_facing_frame'] = self._kin_agent[idx].get_facing_transform(self.get_ground_height()) data['kin_com'], data['kin_com_vel'] = self._kin_agent[idx].get_com_and_com_vel() return data def reward_max(self): return 1.0 def reward_min(self): return 0.0 def get_task_error(self, idx, data_prev, data_next, action): error = {} sim_agent = self._sim_agent[idx] char_info = sim_agent._char_info data = data_next[idx] sim_root_p, sim_root_Q, sim_root_v, sim_root_w = data['sim_root_pQvw'] sim_link_p, sim_link_Q, sim_link_v, sim_link_w = data['sim_link_pQvw'] sim_joint_p, sim_joint_v = data['sim_joint_pv'] sim_facing_frame = data['sim_facing_frame'] R_sim_f, p_sim_f = conversions.T2Rp(sim_facing_frame) R_sim_f_inv = R_sim_f.transpose() sim_com, sim_com_vel = data['sim_com'], data['sim_com_vel'] kin_root_p, kin_root_Q, kin_root_v, kin_root_w = data['kin_root_pQvw'] kin_link_p, kin_link_Q, kin_link_v, kin_link_w = data['kin_link_pQvw'] kin_joint_p, kin_joint_v = data['kin_joint_pv'] kin_facing_frame = data['kin_facing_frame'] R_kin_f, p_kin_f = conversions.T2Rp(kin_facing_frame) R_kin_f_inv = R_kin_f.transpose() kin_com, kin_com_vel = data['kin_com'], data['kin_com_vel'] indices = range(len(sim_joint_p)) if 'pose_pos' in self._reward_names[idx]: error['pose_pos'] = 0.0 for j in indices: joint_type = sim_agent.get_joint_type(j) if joint_type == self._pb_client.JOINT_FIXED: continue elif joint_type == self._pb_client.JOINT_SPHERICAL: dQ = self._pb_client.getDifferenceQuaternion(sim_joint_p[j], kin_joint_p[j]) _, diff_pose_pos = self._pb_client.getAxisAngleFromQuaternion(dQ) else: diff_pose_pos = sim_joint_p[j] - kin_joint_p[j] error['pose_pos'] += char_info.joint_weight[j] * np.dot(diff_pose_pos, diff_pose_pos) if len(indices) > 0: error['pose_pos'] /= len(indices) if 'pose_vel' in self._reward_names[idx]: error['pose_vel'] = 0.0 for j in indices: joint_type = sim_agent.get_joint_type(j) if joint_type == self._pb_client.JOINT_FIXED: continue else: diff_pose_vel = sim_joint_v[j] - kin_joint_v[j] error['pose_vel'] += char_info.joint_weight[j] * np.dot(diff_pose_vel, diff_pose_vel) if len(indices) > 0: error['pose_vel'] /= len(indices) if 'ee' in self._reward_names[idx]: error['ee'] = 0.0 for j in char_info.end_effector_indices: sim_ee_local = np.dot(R_sim_f_inv, sim_link_p[j]-p_sim_f) kin_ee_local = np.dot(R_kin_f_inv, kin_link_p[j]-p_kin_f) diff_pos = sim_ee_local - kin_ee_local error['ee'] += np.dot(diff_pos, diff_pos) if len(char_info.end_effector_indices) > 0: error['ee'] /= len(char_info.end_effector_indices) if 'root' in self._reward_names[idx]: diff_root_p = sim_root_p - kin_root_p _, diff_root_Q = self._pb_client.getAxisAngleFromQuaternion( self._pb_client.getDifferenceQuaternion(sim_root_Q, kin_root_Q)) diff_root_v = sim_root_v - kin_root_v diff_root_w = sim_root_w - kin_root_w error['root'] = 1.0 * np.dot(diff_root_p, diff_root_p) + \ 0.1 * np.dot(diff_root_Q, diff_root_Q) + \ 0.01 * np.dot(diff_root_v, diff_root_v) + \ 0.001 * np.dot(diff_root_w, diff_root_w) if 'com' in self._reward_names[idx]: diff_com = np.dot(R_sim_f_inv, sim_com-p_sim_f) - np.dot(R_kin_f_inv, kin_com-p_kin_f) diff_com_vel = sim_com_vel - kin_com_vel error['com'] = 1.0 * np.dot(diff_com, diff_com) + \ 0.1 * np.dot(diff_com_vel, diff_com_vel) return error def inspect_end_of_episode_task(self): eoe_reason = [] for i in range(self._num_agent): check = self.get_current_time() >= self._ref_motion[i].length() if check: eoe_reason.append('[%s] end_of_motion'%self._sim_agent[i].get_name()) return eoe_reason def inspect_end_of_episode_per_agent(self, idx): eoe_reason = super().inspect_end_of_episode_per_agent(idx) return eoe_reason def get_ground_height(self): return 0.0 def get_current_time(self): return self._start_time + self.get_elapsed_time() def sample_ref_motion(self): ref_indices = [] ref_motions = [] for i in range(self._num_agent): idx = np.random.randint(len(self._ref_motion_all[i])) ref_indices.append(idx) ref_motions.append(self._ref_motion_all[i][idx]) if self._verbose: print('Ref. motions selected: ', ref_indices) return ref_motions if __name__ == '__main__': import env_renderer as er import render_module as rm import argparse from fairmotion.viz.utils import TimeChecker rm.initialize() def arg_parser(): parser = argparse.ArgumentParser() parser.add_argument('--config', required=True, type=str) return parser class EnvRenderer(er.EnvRenderer): def __init__(self, **kwargs): super().__init__(**kwargs) self.time_checker_auto_play = TimeChecker() self.reset() def reset(self): self.env.reset() def one_step(self): # a = np.zeros(100) self.env.step() def extra_render_callback(self): if self.rm.flag['follow_cam']: p, _, _, _ = env._sim_agent[0].get_root_state() self.rm.viewer.update_target_pos(p, ignore_z=True) self.env.render(self.rm) def extra_idle_callback(self): time_elapsed = self.time_checker_auto_play.get_time(restart=False) if self.rm.flag['auto_play'] and time_elapsed >= self.env._dt_act: self.time_checker_auto_play.begin() self.one_step() def extra_keyboard_callback(self, key): if key == b'r': self.reset() elif key == b'O': size = np.random.uniform(0.1, 0.3, 3) p, Q, v, w = self.env._agent[0].get_root_state() self.env._obs_manager.throw(p, size=size) print('=====Humanoid Imitation Environment=====') args = arg_parser().parse_args() env = Env(args.config) cam = rm.camera.Camera(pos=np.array([12.0, 0.0, 12.0]), origin=np.array([0.0, 0.0, 0.0]), vup=np.array([0.0, 0.0, 1.0]), fov=30.0) renderer = EnvRenderer(env=env, cam=cam) renderer.run() ================================================ FILE: env_humanoid_tracking.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. import os ''' This forces the environment to use only 1 cpu when running. This could be helpful when launching multiple environment simulatenously. ''' os.environ['OPENBLAS_NUM_THREADS'] = '1' os.environ['MKL_NUM_THREADS'] = '1' # os.environ['CUDA_VISIBLE_DEVICES'] = '-1' import numpy as np import copy import pybullet as pb import pybullet_data from bullet import bullet_client from bullet import bullet_utils as bu from fairmotion.ops import conversions from fairmotion.ops import math from fairmotion.utils import constants import sim_agent import sim_obstacle import importlib.util class Env(object): ''' This environment defines a base environment where the simulated characters exist and they are controlled by tracking controllers ''' def __init__(self, fps_sim, fps_act, char_info_module, sim_char_file, ref_motion_scale, actuation, self_collision=None, contactable_body=None, verbose=False, ): self._num_agent = len(sim_char_file) assert self._num_agent > 0 assert self._num_agent == len(char_info_module) assert self._num_agent == len(ref_motion_scale) self._char_info = [] for i in range(self._num_agent): ''' Load Character Info Moudle ''' spec = importlib.util.spec_from_file_location( "char_info%d"%(i), char_info_module[i]) char_info = importlib.util.module_from_spec(spec) spec.loader.exec_module(char_info) self._char_info.append(char_info) ''' Modfiy Contactable Body Parts ''' if contactable_body: contact_allow_all = True if 'all' in contactable_body else False for joint in list(char_info.contact_allow_map.keys()): char_info.contact_allow_map[joint] = \ contact_allow_all or char_info.joint_name[joint] in contactable_body self._v_up = self._char_info[0].v_up_env ''' Define PyBullet Client ''' self._pb_client = bullet_client.BulletClient( connection_mode=pb.DIRECT, options=' --opengl2') self._pb_client.setAdditionalSearchPath(pybullet_data.getDataPath()) ''' timestep for physics simulation ''' self._dt_sim = 1.0/fps_sim ''' timestep for control of dynamic controller ''' self._dt_act = 1.0/fps_act if fps_sim%fps_act != 0: raise Exception('FPS_SIM should be a multiples of FPS_ACT') self._num_substep = fps_sim//fps_act self._verbose = verbose self.setup_physics_scene(sim_char_file, self._char_info, ref_motion_scale, self_collision, actuation) ''' Elapsed time after the environment starts ''' self._elapsed_time = 0.0 ''' For tracking the length of current episode ''' self._episode_len = 0.0 ''' Create a Manager for Handling Obstacles ''' self._obs_manager = sim_obstacle.ObstacleManager( self._pb_client, self._dt_act, self._char_info[0].v_up_env) ''' Save the initial pybullet state to clear all thing before calling reset ''' self._init_state = None self.reset() self._init_state = self._pb_client.saveState() def setup_physics_scene(self, sim_char_file, char_info, ref_motion_scale, self_collision, actuation): self._pb_client.resetSimulation() self.create_ground() self._agent = [] for i in range(self._num_agent): self._agent.append(sim_agent.SimAgent(name='sim_agent_%d'%(i), pybullet_client=self._pb_client, model_file=sim_char_file[i], char_info=char_info[i], ref_scale=ref_motion_scale[i], self_collision=self_collision[i], actuation=actuation[i], kinematic_only=False, verbose=self._verbose)) def create_ground(self): ''' Create Plane ''' if np.allclose(np.array([0.0, 0.0, 1.0]), self._v_up): R_plane = constants.eye_R() else: R_plane = math.R_from_vectors(np.array([0.0, 0.0, 1.0]), self._v_up) self._plane_id = \ self._pb_client.loadURDF( "plane_implicit.urdf", [0, 0, 0], conversions.R2Q(R_plane), useMaximalCoordinates=True) self._pb_client.changeDynamics(self._plane_id, linkIndex=-1, lateralFriction=0.9) ''' Dynamics parameters ''' assert np.allclose(np.linalg.norm(self._v_up), 1.0) gravity = -9.8 * self._v_up self._pb_client.setGravity(gravity[0], gravity[1], gravity[2]) self._pb_client.setTimeStep(self._dt_sim) self._pb_client.setPhysicsEngineParameter(numSubSteps=2) self._pb_client.setPhysicsEngineParameter(numSolverIterations=10) # self._pb_client.setPhysicsEngineParameter(solverResidualThreshold=1e-10) def check_collision(self, body_id1, body_id2, link_id1=None, link_id2=None): ''' collision between two bodies ''' pts = self._pb_client.getContactPoints( bodyA=body_id1, bodyB=body_id2, linkIndexA=link_id1, linkIndexB=link_id2) return len(p) > 0 # def check_falldown(self, agent, plane_id=None): # ''' check if any non-allowed body part hits the ground ''' # if plane_id is None: plane_id = self._plane_id # pts = self._pb_client.getContactPoints() # for p in pts: # part = None # #ignore self-collision # if p[1] == p[2]: continue # if p[1] == agent._body_id and p[2] == plane_id: part = p[3] # if p[2] == agent._body_id and p[1] == plane_id: part = p[4] # #ignore collision of other agents # if part == None: continue # if not agent._char_info.contact_allow_map[part]: return True # return False def check_falldown(self, agent, plane_id=None): ''' check if any non-allowed body part hits the ground ''' if plane_id is None: plane_id = self._plane_id pts = self._pb_client.getContactPoints( bodyA=agent._body_id, bodyB=plane_id) for p in pts: part = p[3] if p[1] == agent._body_id else p[4] if agent._char_info.contact_allow_map[part]: continue else: return True return False def is_sim_div(self, agent): ''' TODO: check divergence of simulation ''' return False def step(self, target_poses=[]): ''' One Step-forward Simulation ''' ''' Increase elapsed time ''' self._elapsed_time += self._dt_act self._episode_len += self._dt_act ''' Update simulation ''' for _ in range(self._num_substep): for i, target_pose in enumerate(target_poses): self._agent[i].actuate(pose=target_pose, vel=None) self._pb_client.stepSimulation() self._obs_manager.update() def reset(self, time=0.0, poses=None, vels=None, pb_state_id=None): ''' remove obstacles in the scene ''' self._obs_manager.clear() ''' Restore internal pybullet state by uisng the saved info when Env was initially created ''' if pb_state_id is not None: self._pb_client.restoreState(pb_state_id) self._elapsed_time = time if poses is None: if self._init_state is not None: self._pb_client.restoreState(self._init_state) else: for i in range(self._num_agent): pose = poses[i] vel = None if vels is None else vels[i] self._agent[i].set_pose(pose, vel) self._episode_len = 0.0 def add_noise_to_pose_vel(self, agent, pose, vel=None, return_as_copied=True): ''' Add a little bit of noise to the given pose and velocity ''' ref_pose = copy.deepcopy(pose) if return_as_copied else pose if vel: ref_vel = copy.deepcopy(vel) if return_as_copied else vel dof_cnt = 0 for j in agent._joint_indices: joint_type = agent.get_joint_type(j) ''' Ignore fixed joints ''' if joint_type == self._pb_client.JOINT_FIXED: continue ''' Ignore if there is no corresponding joint ''' if agent._char_info.bvh_map[j] == None: continue T = ref_pose.get_transform(agent._char_info.bvh_map[j], local=True) R, p = conversions.T2Rp(T) if joint_type == self._pb_client.JOINT_SPHERICAL: dR = math.random_rotation( mu_theta=agent._char_info.noise_pose[j][0], sigma_theta=agent._char_info.noise_pose[j][1], lower_theta=agent._char_info.noise_pose[j][2], upper_theta=agent._char_info.noise_pose[j][3]) dof_cnt += 3 elif joint_type == self._pb_client.JOINT_REVOLUTE: theta = math.truncnorm( mu=agent._char_info.noise_pose[j][0], sigma=agent._char_info.noise_pose[j][1], lower=agent._char_info.noise_pose[j][2], upper=agent._char_info.noise_pose[j][3]) joint_axis = agent.get_joint_axis(j) dR = conversions.A2R(joint_axis*theta) dof_cnt += 1 else: raise NotImplementedError T_new = conversions.Rp2T(np.dot(R, dR), p) ref_pose.set_transform(agent._char_info.bvh_map[j], T_new, do_ortho_norm=False, local=True) if vel is not None: dw = math.truncnorm( mu=np.full(3, agent._char_info.noise_vel[j][0]), sigma=np.full(3, agent._char_info.noise_vel[j][1]), lower=np.full(3, agent._char_info.noise_vel[j][2]), upper=np.full(3, agent._char_info.noise_vel[j][3])) ref_vel.data_local[j][:3] += dw return ref_pose, ref_vel def render(self, rm, ground_height=0.0): colors = rm.COLORS_FOR_AGENTS rm.gl.glEnable(rm.gl.GL_LIGHTING) rm.gl.glEnable(rm.gl.GL_BLEND) rm.gl.glBlendFunc(rm.gl.GL_SRC_ALPHA, rm.gl.GL_ONE_MINUS_SRC_ALPHA) for i in range(self._num_agent): sim_agent = self._agent[i] char_info = self._char_info[i] if rm.flag['sim_model']: rm.gl.glEnable(rm.gl.GL_DEPTH_TEST) if rm.flag['shadow']: rm.gl.glPushMatrix() d = np.array([1, 1, 1]) d = d - math.projectionOnVector(d, char_info.v_up_env) offset = (0.001 + ground_height) * char_info.v_up_env rm.gl.glTranslatef(offset[0], offset[1], offset[2]) rm.gl.glScalef(d[0], d[1], d[2]) rm.bullet_render.render_model(self._pb_client, sim_agent._body_id, draw_link=True, draw_link_info=False, draw_joint=False, draw_joint_geom=False, ee_indices=None, color=[0.5,0.5,0.5,1.0], lighting=False) rm.gl.glPopMatrix() rm.bullet_render.render_model(self._pb_client, sim_agent._body_id, draw_link=True, draw_link_info=True, draw_joint=rm.flag['joint'], draw_joint_geom=True, ee_indices=char_info.end_effector_indices, color=colors[i]) if rm.flag['collision'] and self._elapsed_time > 0.0: rm.gl.glPushAttrib(rm.gl.GL_LIGHTING|rm.gl.GL_DEPTH_TEST|rm.gl.GL_BLEND) rm.gl.glEnable(rm.gl.GL_BLEND) rm.bullet_render.render_contacts(self._pb_client, sim_agent._body_id) rm.gl.glPopAttrib() if rm.flag['com_vel']: p, Q, v, w = sim_agent.get_root_state() p, v = sim_agent.get_com_and_com_vel() rm.gl_render.render_arrow(p, p+v, D=0.01, color=[0, 0, 0, 1]) if rm.flag['facing_frame']: rm.gl.glPushAttrib(rm.gl.GL_LIGHTING|rm.gl.GL_DEPTH_TEST|rm.gl.GL_BLEND) rm.gl.glEnable(rm.gl.GL_BLEND) rm.gl_render.render_transform( sim_agent.get_facing_transform(ground_height), scale=0.5, use_arrow=True) rm.gl.glPopAttrib() if rm.flag['obstacle']: self._obs_manager.render() if __name__ == '__main__': import env_renderer as er import render_module as rm from fairmotion.viz.utils import TimeChecker rm.initialize() class EnvRenderer(er.EnvRenderer): def __init__(self, **kwargs): super().__init__(**kwargs) self.time_checker_auto_play = TimeChecker() self.reset() def reset(self): self.env.reset() def one_step(self): # a = np.zeros(100) self.env.step() def extra_render_callback(self): self.env.render(self.rm) def extra_idle_callback(self): time_elapsed = self.time_checker_auto_play.get_time(restart=False) if self.rm.flag['auto_play'] and time_elapsed >= self.env._dt_act: self.time_checker_auto_play.begin() self.one_step() def extra_keyboard_callback(self, key): if key == b'r': self.reset() elif key == b'O': size = np.random.uniform(0.1, 0.3, 3) p, Q, v, w = self.env._agent[0].get_root_state() self.env._obs_manager.throw(p, size=size) print('=====Motion Tracking Controller=====') env = Env(fps_sim=480, fps_act=30, verbose=False, char_info_module=['amass_char_info.py'], sim_char_file=['data/character/amass.urdf'], ref_motion_scale=[1.0], self_collision=[True], actuation=["spd"]) cam = rm.camera.Camera(pos=np.array([12.0, 0.0, 12.0]), origin=np.array([0.0, 0.0, 0.0]), vup=np.array([0.0, 0.0, 1.0]), fov=30.0) renderer = EnvRenderer(env=env, cam=cam) renderer.run() ================================================ FILE: env_renderer.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. import numpy as np import render_module as rm from fairmotion.viz import glut_viewer rm.initialize() def axis_to_str(axis): if np.allclose(axis, np.array([1.0, 0.0, 0.0])): return 'x' elif np.allclose(axis, np.array([0.0, 1.0, 0.0])): return 'y' elif np.allclose(axis, np.array([0.0, 0.0, 1.0])): return 'z' else: raise Exception class EnvRenderer(glut_viewer.Viewer): def __init__(self, env=None, title="env_renderer", cam=None, size=(1200, 900)): super().__init__(title, cam, size) self.rm = rm self.env = env def save_screen(self, dir, name): self.rm.viewer.save_screen(dir=dir, name=name) def render_ground(self, size=[40.0, 40.0], dsize=[2.0, 2.0], axis='z', origin=True, use_arrow=True, circle_cut=True): if self.rm.tex_id_ground is None: self.rm.tex_id_ground = \ self.rm.gl_render.load_texture(self.rm.file_tex_ground) self.rm.gl_render.render_ground_texture( self.rm.tex_id_ground, size=size, dsize=dsize, axis=axis, origin=origin, use_arrow=use_arrow, circle_cut=circle_cut) def extra_keyboard_callback(self, key): pass def extra_render_callback(self): pass def extra_idle_callback(self): pass def extra_overlay_callback(self): pass def keyboard_callback(self, key): if key in self.rm.toggle: self.rm.flag[self.rm.toggle[key]] = not self.rm.flag[self.rm.toggle[key]] print('Toggled:', self.rm.toggle[key], self.rm.flag[self.rm.toggle[key]]) else: self.extra_keyboard_callback(key) def render_callback(self): if self.rm.flag['ground']: self.render_ground( axis=axis_to_str(self.cam_cur.vup), origin=self.rm.flag['origin']) self.extra_render_callback() def idle_callback(self): self.extra_idle_callback() def overlay_callback(self): if not self.rm.flag['overlay']: return self.extra_overlay_callback() def update_target_pos(self, pos, ignore_x=False, ignore_y=False, ignore_z=False): if np.array_equal(pos, self.cam_cur.origin): return d = pos - self.cam_cur.origin if ignore_x: d[0] = 0.0 if ignore_y: d[1] = 0.0 if ignore_z: d[2] = 0.0 self.cam_cur.translate(d) ================================================ FILE: render_module.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. import numpy as np viewer = None camera = None gl_render = None bullet_render = None gl = None glu = None glut = None flag = {} flag['follow_cam'] = True flag['ground'] = True flag['origin'] = False flag['shadow'] = True flag['sim_model'] = True flag['kin_model'] = True flag['joint'] = False flag['com_vel'] = False flag['collision'] = True flag['overlay'] = True flag['target_pose'] = False flag['auto_play'] = False flag['fog'] = False flag['facing_frame'] = False flag['obstacle'] = True toggle = {} toggle[b'0'] = 'follow_cam' toggle[b'1'] = 'ground' toggle[b'2'] = 'origin' toggle[b'3'] = 'shadow' toggle[b'4'] = 'sim_model' toggle[b'5'] = 'kin_model' toggle[b'6'] = 'joint' toggle[b'7'] = 'com_vel' toggle[b'8'] = 'collision' toggle[b'9'] = 'overlay' toggle[b't'] = 'target_pose' toggle[b'a'] = 'auto_play' toggle[b'f'] = 'fog' toggle[b'F'] = 'facing_frame' toggle[b'o'] = 'obstacle' tex_id_ground = None file_tex_ground = "data/image/grid2.png" COLORS_FOR_AGENTS = [ np.array([30, 120, 180, 255])/255, np.array([215, 40, 40, 255])/255, np.array([225, 120, 190, 255])/255, np.array([150, 100, 190, 255])/255, np.array([140, 90, 80, 255])/255, np.array([50, 160, 50, 255])/255, np.array([255, 125, 15, 255])/255, np.array([125, 125, 125, 255])/255, np.array([255, 0, 255, 255])/255, np.array([0, 255, 125, 255])/255, ] COLOR_AGENT = np.array([85, 160, 173, 255])/255.0 COLORS_FOR_EXPERTS = [ np.array([30, 120, 180, 255])/255, np.array([215, 40, 40, 255])/255, np.array([225, 120, 190, 255])/255, np.array([150, 100, 190, 255])/255, np.array([140, 90, 80, 255])/255, np.array([50, 160, 50, 255])/255, np.array([255, 125, 15, 255])/255, np.array([125, 125, 125, 255])/255, np.array([255, 0, 255, 255])/255, np.array([0, 255, 125, 255])/255, ] initialized = False def initialize(): global initialized if initialized: return global viewer, gl_render, camera, bullet_render, gl, glu, glut from fairmotion.viz import glut_viewer as viewer from fairmotion.viz import camera from fairmotion.viz import gl_render from bullet import bullet_render import OpenGL.GL as gl import OpenGL.GLU as glu import OpenGL.GLUT as glut initialized = True ================================================ FILE: rllib_driver.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. import argparse import os import yaml import ray from ray import tune from ray.tune.registry import register_env def arg_parser(): parser = argparse.ArgumentParser() ''' Specification file of the expriment ''' parser.add_argument("--spec", required=True, type=str) ''' Mode for running an experiment ''' parser.add_argument("--mode", required=True, choices=['train', 'load']) ''' ''' parser.add_argument("--checkpoint", type=str, default=None) ''' ''' parser.add_argument("--num_workers", type=int, default=None) ''' ''' parser.add_argument("--num_cpus", type=int, default=1) ''' ''' parser.add_argument("--num_gpus", type=int, default=0) ''' ''' parser.add_argument("--num_envs_per_worker", type=int, default=None) ''' ''' parser.add_argument("--num_cpus_per_worker", type=int, default=None) ''' ''' parser.add_argument("--num_gpus_per_worker", type=int, default=None) ''' Directory where the environment and related files are stored ''' parser.add_argument("--project_dir", type=str, default=None) ''' Directory where intermediate results are saved ''' parser.add_argument("--local_dir", type=str, default=None) ''' Verbose ''' parser.add_argument("--verbose", action='store_true') ''' ''' parser.add_argument("--ip_head", type=str, default=None) ''' ''' parser.add_argument("--password", type=str, default=None) return parser if __name__ == "__main__": args = arg_parser().parse_args() with open(args.spec) as f: spec = yaml.load(f, Loader=yaml.FullLoader) config = spec['config'] ''' Register environment to learn according to the input specification file ''' if config['env'] == "HumanoidImitation": import rllib_env_imitation as env_module else: raise NotImplementedError("Unknown Environment") register_env(config['env'], lambda config: env_module.env_cls(config)) ''' Register custom model to use if it exists ''' framework = config.get('framework') if config.get('model'): custom_model = config.get('model').get('custom_model') if custom_model: if framework=='torch': import rllib_model_custom_torch else: raise NotImplementedError("Tensorflow is not supported!") ''' Validate configurations and overide values by arguments ''' if args.local_dir is not None: spec.update({'local_dir': args.local_dir}) if args.project_dir is not None: assert os.path.exists(args.project_dir) config['env_config']['project_dir'] = args.project_dir if config['model'].get('custom_model_config'): config['model']['custom_model_config'].update( {'project_dir': config['env_config']['project_dir']}) if args.verbose: config['env_config'].update({'verbose': args.verbose}) if args.checkpoint is not None: assert os.path.exists(args.checkpoint) if args.num_workers is not None: config.update({'num_workers': args.num_workers}) if args.num_gpus is not None: config.update({'num_gpus': args.num_gpus}) if args.num_envs_per_worker: config.update({'num_envs_per_worker': args.num_envs_per_worker}) if args.num_cpus_per_worker: config.update({'num_cpus_per_worker': args.num_cpus_per_worker}) if args.num_gpus_per_worker: config.update({'num_gpus_per_worker': args.num_gpus_per_worker}) if args.mode == "train": if not os.path.exists(spec['local_dir']): raise Exception( "The directory does not exist: %s"%spec['local_dir']) config_override = env_module.config_override(spec) config.update(config_override) if args.ip_head: # tmp_dir = os.path.join(spec['local_dir'], os.path.join('tmp/', spec['name'])) if args.password: ray.init(address=args.ip_head, redis_password=args.password) else: ray.init(address=args.ip_head) else: assert args.num_cpus is not None assert args.num_gpus is not None ray.init(num_cpus=args.num_cpus, num_gpus=args.num_gpus) def adjust_config_for_loading(config, alg): config["num_workers"] = 1 config['num_envs_per_worker'] = 1 config['num_cpus_per_worker'] = 1 config['num_gpus_per_worker'] = 0 config['remote_worker_envs'] = False def adjust_config(config, alg): rollout_fragment_length = config.get('rollout_fragment_length') num_workers = config.get('num_workers') num_envs_per_worker = config.get('num_envs_per_worker') train_batch_size = config.get('train_batch_size') ''' Set rollout_fragment_length value so that workers can genertate train_batch_size tuples correctly ''' rollout_fragment_length = \ max(train_batch_size // (num_workers * num_envs_per_worker), 100) while rollout_fragment_length * num_workers * num_envs_per_worker \ < train_batch_size: rollout_fragment_length += 1 config['rollout_fragment_length'] = rollout_fragment_length adjust_config(config, spec['run']) if args.mode == "load": adjust_config_for_loading(config, spec['run']) if spec["run"] == "PPO": from ray.rllib.agents.ppo import PPOTrainer as Trainer else: raise NotImplementedError("Not a supported algorithm") trainer = Trainer(env=env_module.env_cls, config=config) if args.checkpoint is not None: trainer.restore(args.checkpoint) env_module.rm.initialize() env = env_module.env_cls(config['env_config']) cam = env_module.default_cam() renderer = env_module.EnvRenderer(trainer=trainer, env=env, cam=cam) renderer.run() else: tune.run( spec['run'], name=spec['name'], stop=spec['stop'], local_dir=spec['local_dir'], checkpoint_freq=spec['checkpoint_freq'], checkpoint_at_end=spec['checkpoint_at_end'], config=config, restore=args.checkpoint, sync_to_driver=False, ) ================================================ FILE: rllib_env_imitation.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. import copy import numpy as np import argparse import random import gym from gym.spaces import Box import env_humanoid_imitation as my_env import env_renderer as er import render_module as rm import os class HumanoidImitation(gym.Env): def __init__(self, env_config): self.base_env = my_env.Env(env_config) assert self.base_env._num_agent == 1 ob_scale = 1000.0 dim_state = self.base_env.dim_state(0) dim_state_body = self.base_env.dim_state_body(0) dim_state_task = self.base_env.dim_state_task(0) dim_action = self.base_env.dim_action(0) action_range_min, action_range_max = self.base_env.action_range(0) self.observation_space = \ Box(-ob_scale * np.ones(dim_state), ob_scale * np.ones(dim_state), dtype=np.float64) self.observation_space_body = \ Box(-ob_scale * np.ones(dim_state_body), ob_scale * np.ones(dim_state_body), dtype=np.float64) self.observation_space_task = \ Box(-ob_scale * np.ones(dim_state_task), ob_scale * np.ones(dim_state_task), dtype=np.float64) self.action_space = \ Box(action_range_min, action_range_max, dtype=np.float64) def state(self): return self.base_env.state(idx=0) def reset(self, start_time=None, add_noise=None): if not self.base_env._initialized: self.base_env.create() self.base_env.reset({ 'start_time': start_time, 'add_noise': add_noise, }) return self.base_env.state(idx=0) def step(self, action): rew, info = self.base_env.step([action]) obs = self.state() eoe = self.base_env._end_of_episode return obs, rew[0], eoe, info[0] class EnvRenderer(er.EnvRenderer): def __init__(self, trainer, **kwargs): from fairmotion.viz.utils import TimeChecker super().__init__(**kwargs) self.trainer = trainer self.time_checker_auto_play = TimeChecker() self.explore = False def one_step(self): s1 = self.env.state() a = self.trainer.compute_action(s1, explore=self.explore) s2, rew, eoe, info = self.env.step(a) def extra_render_callback(self): if self.rm.flag['follow_cam']: p, _, _, _ = self.env.base_env._sim_agent[0].get_root_state() self.update_target_pos(p, ignore_z=True) self.env.base_env.render(self.rm) def extra_overlay_callback(self): model = self.trainer.get_policy().model if hasattr(model, 'gate_function'): expert_weights = model.gate_function() num_experts = model.num_experts() w, h = self.window_size w_bar, h_bar = 150, 20 origin = np.array([0.95*w-w_bar, 0.95*h-h_bar]) pos = origin.copy() for i in reversed(range(num_experts)): self.rm.gl_render.render_text( "Expert%d"%(i), pos=pos-np.array([75, -0.8*h_bar]), font=self.rm.glut.GLUT_BITMAP_9_BY_15) w_i = expert_weights[0][i] if expert_weights is not None else 0.0 self.rm.gl_render.render_progress_bar_2D_horizontal( w_i, origin=pos, width=w_bar, height=h_bar, color_input=self.rm.COLORS_FOR_EXPERTS[i]) pos += np.array([0.0, -h_bar]) def extra_idle_callback(self): time_elapsed = self.time_checker_auto_play.get_time(restart=False) if self.rm.flag['auto_play'] and time_elapsed >= self.env.base_env._dt_con: self.time_checker_auto_play.begin() self.one_step() def extra_keyboard_callback(self, key): if key == b'r': s = self.env.reset() elif key == b'R': s = self.env.reset(start_time=0.0) elif key == b' ': self.time_checker_auto_play.begin() self.one_step() elif key == b'e': self.explore = not self.explore print('Exploration:', self.explore) elif key == b'c': ''' Read a directory for saving images and try to create it ''' subdir = input("Enter subdirectory for screenshot file: ") dir = os.path.join("data/screenshot/", subdir) try: os.makedirs(dir, exist_ok = True) except OSError: print("Invalid Subdirectory") return for i in range(1): try: os.makedirs(dir, exist_ok = True) except OSError: print("Invalid Subdirectory") continue cnt_screenshot = 0 while True: name = 'screenshot_%04d'%(cnt_screenshot) self.one_step() self.render() self.save_screen(dir=dir, name=name) print('\rsave_screen(%4.4f) / %s' % \ (self.env.base_env.get_elapsed_time(), os.path.join(dir,name)), end=" ") cnt_screenshot += 1 if self.env.base_env._end_of_episode: break print("\n") def default_cam(): return rm.camera.Camera(pos=np.array([0.0, 3.0, 2.0]), origin=np.array([0.0, 0.0, 0.0]), vup=np.array([0.0, 0.0, 1.0]), fov=60.0) env_cls = HumanoidImitation def config_override(spec): model_config = copy.deepcopy(spec["config"]["model"]) config = { # "callbacks": {}, "model": model_config, } return config ================================================ FILE: rllib_model_custom_torch.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. import logging import numpy as np import os from ray.rllib.models.modelv2 import ModelV2 from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 from ray.rllib.models.torch.misc import SlimFC, normc_initializer from ray.rllib.policy.rnn_sequencing import add_time_dimension from ray.rllib.utils.annotations import override from ray.rllib.utils.framework import get_activation_fn from ray.rllib.utils import try_import_torch from ray.rllib.models import ModelCatalog torch, nn = try_import_torch() import torch.nn.functional as F logger = logging.getLogger(__name__) class AppendLogStd(nn.Module): ''' An appending layer for free_log_std. ''' def __init__(self, type, init_val, dim): super().__init__() self.type = type self.init_val = init_val if self.type=="constant": self.log_std = torch.as_tensor([init_val] * dim) elif self.type=="state_independent": self.log_std = torch.nn.Parameter( torch.as_tensor([init_val] * dim)) self.register_parameter("log_std", self.log_std) else: raise NotImplementedError def forward(self, x): assert x.shape[-1] == self.log_std.shape[-1] shape = list(x.shape) for i in range(0, len(shape)-1): shape[i] = 1 log_std = torch.reshape(self.log_std, shape) shape = list(x.shape) shape[-1] = 1 log_std = log_std.repeat(shape) out = torch.cat([x, log_std], axis=-1) return out class FC(nn.Module): ''' A network with fully connected layers. ''' def __init__(self, size_in, size_out, hiddens, activations, init_weights, append_log_std=False, log_std_type='constant', sample_std=1.0): super().__init__() layers = [] prev_layer_size = size_in for i, size_hidden in enumerate(hiddens+[size_out]): layers.append( SlimFC( in_size=prev_layer_size, out_size=size_hidden, initializer=normc_initializer( init_weights[i]), activation_fn=get_activation_fn( activations[i], framework="torch"))) prev_layer_size = size_hidden if append_log_std: layers.append(AppendLogStd( type=log_std_type, init_val=np.log(sample_std), dim=size_out)) self._model = nn.Sequential(*layers) def forward(self, x): return self._model(x) class FullyConnectedPolicy(TorchModelV2, nn.Module): ''' A policy that generates action and value with FCNN ''' DEFAULT_CONFIG = { "log_std_type": "constant", "sample_std": 1.0, "policy_fn_hiddens": [128, 128], "policy_fn_activations": ["relu", "relu", None], "policy_fn_init_weights": [1.0, 1.0, 0.01], "value_fn_hiddens": [128, 128], "value_fn_activations": ["relu", "relu", None], "value_fn_init_weights": [1.0, 1.0, 0.01], } """Generic fully connected network.""" def __init__(self, obs_space, action_space, num_outputs, model_config, name, **model_kwargs): TorchModelV2.__init__(self, obs_space, action_space, num_outputs, model_config, name) nn.Module.__init__(self) ''' Load and check configuarations ''' assert num_outputs % 2 == 0, ( "num_outputs must be divisible by two", num_outputs) num_outputs = num_outputs // 2 custom_model_config = FullyConnectedPolicy.DEFAULT_CONFIG.copy() custom_model_config_by_user = model_config.get("custom_model_config") if custom_model_config_by_user: custom_model_config.update(custom_model_config_by_user) log_std_type = custom_model_config.get("log_std_type") assert log_std_type in ["constant", "state_independent"] sample_std = custom_model_config.get("sample_std") assert sample_std > 0.0, "The value shoulde be positive" policy_fn_hiddens = custom_model_config.get("policy_fn_hiddens") policy_fn_activations = custom_model_config.get("policy_fn_activations") policy_fn_init_weights = custom_model_config.get("policy_fn_init_weights") assert len(policy_fn_hiddens) > 0 assert len(policy_fn_hiddens)+1 == len(policy_fn_activations) assert len(policy_fn_hiddens)+1 == len(policy_fn_init_weights) value_fn_hiddens = custom_model_config.get("value_fn_hiddens") value_fn_activations = custom_model_config.get("value_fn_activations") value_fn_init_weights = custom_model_config.get("value_fn_init_weights") assert len(value_fn_hiddens) > 0 assert len(value_fn_hiddens)+1 == len(value_fn_activations) assert len(value_fn_hiddens)+1 == len(value_fn_init_weights) dim_state = int(np.product(obs_space.shape)) ''' Construct the policy function ''' self._policy_fn = FC( size_in=dim_state, size_out=num_outputs, hiddens=policy_fn_hiddens, activations=policy_fn_activations, init_weights=policy_fn_init_weights, append_log_std=True, log_std_type=log_std_type, sample_std=sample_std) ''' Construct the value function ''' self._value_fn = FC( size_in=dim_state, size_out=1, hiddens=value_fn_hiddens, activations=value_fn_activations, init_weights=value_fn_init_weights, append_log_std=False) self._cur_value = None @override(TorchModelV2) def forward(self, input_dict, state, seq_lens): obs = input_dict["obs_flat"].float() obs = obs.reshape(obs.shape[0], -1) logits = self._policy_fn(obs) self._cur_value = self._value_fn(obs).squeeze(1) return logits, state @override(TorchModelV2) def value_function(self): assert self._cur_value is not None, "must call forward() first" return self._cur_value def save_policy_weights(self, file): torch.save(self._policy_fn.state_dict(), file) class MOEPolicyBase(TorchModelV2, nn.Module): ''' A base policy with Mixture-of-Experts structure ''' DEFAULT_CONFIG = { "log_std_type": "constant", "sample_std": 1.0, "expert_hiddens": [ [128, 128], [128, 128], [128, 128], ], "expert_activations": [ ["relu", "relu", None], ["relu", "relu", None], ["relu", "relu", None], ], "expert_init_weights": [ [1.0, 1.0, 0.01], [1.0, 1.0, 0.01], [1.0, 1.0, 0.01], ], "expert_log_std_types": [ 'constant', 'constant', 'constant', ], "expert_sample_stds": [ 0.1, 0.1, 0.1, ], "expert_checkpoints": [ None, None, None, ], "expert_learnable": [ True, True, True, ], "gate_fn_hiddens": [128, 128], "gate_fn_activations": ["relu", "relu", None], "gate_fn_init_weights": [1.0, 1.0, 0.01], "gate_fn_learnable": True, "value_fn_hiddens": [128, 128], "value_fn_activations": ["relu", "relu", None], "value_fn_init_weights": [1.0, 1.0, 0.01], } def __init__(self, obs_space, action_space, num_outputs, model_config, name, **model_kwargs): TorchModelV2.__init__(self, obs_space, action_space, num_outputs, model_config, name) nn.Module.__init__(self) ''' Load and check configuarations ''' assert num_outputs % 2 == 0, ( "num_outputs must be divisible by two", num_outputs) num_outputs = num_outputs // 2 custom_model_config = MOEPolicyBase.DEFAULT_CONFIG.copy() custom_model_config_by_user = model_config.get("custom_model_config") if custom_model_config_by_user: custom_model_config.update(custom_model_config_by_user) log_std_type = custom_model_config.get("log_std_type") assert log_std_type in ["constant", "state_independent"] sample_std = custom_model_config.get("sample_std") assert sample_std > 0.0, "The value shoulde be positive" expert_hiddens = custom_model_config.get("expert_hiddens") expert_activations = custom_model_config.get("expert_activations") expert_init_weights = custom_model_config.get("expert_init_weights") expert_log_std_types = custom_model_config.get("expert_log_std_types") expert_sample_stds = custom_model_config.get("expert_sample_stds") expert_checkpoints = custom_model_config.get("expert_checkpoints") expert_learnable = custom_model_config.get("expert_learnable") gate_fn_hiddens = custom_model_config.get("gate_fn_hiddens") gate_fn_activations = custom_model_config.get("gate_fn_activations") gate_fn_init_weights = custom_model_config.get("gate_fn_init_weights") value_fn_hiddens = custom_model_config.get("value_fn_hiddens") value_fn_activations = custom_model_config.get("value_fn_activations") value_fn_init_weights = custom_model_config.get("value_fn_init_weights") dim_state = int(np.product(obs_space.shape)) num_experts = len(expert_hiddens) ''' Construct the gate function ''' self._gate_fn = FC( size_in=dim_state, size_out=num_experts, hiddens=gate_fn_hiddens, activations=gate_fn_activations, init_weights=gate_fn_init_weights, append_log_std=False) ''' Construct experts ''' self._experts = [] for i in range(num_experts): expert = FC( size_in=dim_state, size_out=num_outputs, hiddens=expert_hiddens[i], activations=expert_activations[i], init_weights=expert_init_weights[i], append_log_std=True, log_std_type=expert_log_std_types[i], sample_std=expert_sample_stds[i]) if expert_checkpoints[i]: expert.load_state_dict(torch.load(expert_checkpoints[i])) expert.eval() for name, param in expert.named_parameters(): param.requires_grad = expert_learnable[i] self._experts.append(expert) ''' Construct the value function ''' self._value_fn = FC( size_in=dim_state, size_out=1, hiddens=value_fn_hiddens, activations=value_fn_activations, init_weights=value_fn_init_weights, append_log_std=False) self._num_experts = num_experts self._cur_value = None self._cur_gate_weight = None @override(TorchModelV2) def forward(self, input_dict, state, seq_lens): raise NotImplementedError @override(TorchModelV2) def value_function(self): assert self._cur_value is not None, "must call forward() first" return self._cur_value def gate_function(self): return self._cur_gate_weight def num_experts(self): return self._num_experts class MOEPolicyAdditive(MOEPolicyBase): def __init__(self, obs_space, action_space, num_outputs, model_config, name, **model_kwargs): super().__init__( obs_space, action_space, num_outputs, model_config, name, **model_kwargs) def forward(self, input_dict, state, seq_lens): obs = input_dict["obs_flat"].float() obs = obs.reshape(obs.shape[0], -1) w = F.softmax(self._gate_fn(obs), dim=1) x = 0.0 for i, expert in enumerate(self._experts): x += w[...,i]*expert(obs) logits = x self._cur_gate_weight = w self._cur_value = self._value_fn(obs).squeeze(1) return logits, state class MOEPolicyMultiplicative(MOEPolicyBase): def __init__(self, obs_space, action_space, num_outputs, model_config, name, **model_kwargs): super().__init__( obs_space, action_space, num_outputs, model_config, name, **model_kwargs) def forward(self, input_dict, state, seq_lens): obs = input_dict["obs_flat"].float() obs = obs.reshape(obs.shape[0], -1) w = F.softmax(self._gate_fn(obs), dim=1).unsqueeze(-1) x = torch.stack([expert(obs) for expert in self._experts], dim=1) expert_mean = x[...,:self.num_outputs] expert_std = torch.exp(x[...,self.num_outputs:]) z = w / expert_std std = 1.0 / torch.sum(z, dim=1) logstd = torch.log(std) mean = std * torch.sum(z * expert_mean, dim=1) logits = torch.concat([], ) self._cur_weight = w self._cur_value = self._value_fn(obs).squeeze(1) return logits, state ModelCatalog.register_custom_model("fcnn", FullyConnectedPolicy) ModelCatalog.register_custom_model("moe_additive", MOEPolicyAdditive) ModelCatalog.register_custom_model("moe_multiplicative", MOEPolicyMultiplicative) ================================================ FILE: sim_agent.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. import numpy as np from enum import Enum from bullet import bullet_utils as bu from fairmotion.ops import conversions from fairmotion.ops import math from fairmotion.ops import quaternion from fairmotion.utils import constants from fairmotion.core import motion import warnings class SimAgent(object): ''' This defines a simulated character in the scene. ''' class Actuation(Enum): NONE=0 # No control SPD=1 # Stable PD Control PD=2 # PD Control CPD=3 # PD Control as Constraints of Simulation CP=4 # Position Control as Constraints of Simulation V=5 # Velocity Control as Constraints of Simulation TQ=6 # Torque Control @classmethod def from_string(cls, string): if string=="none": return cls.NONE if string=="spd": return cls.SPD if string=="pd": return cls.PD if string=="cpd": return cls.CPD if string=="cp": return cls.CP if string=="v": return cls.V if string=='tq': return cls.TQ raise NotImplementedError def __init__(self, pybullet_client, model_file, char_info, scale=1.0, # This affects loadURDF ref_scale=1.0, # This will be used when reference motions are appllied to this agent verbose=False, kinematic_only=False, self_collision=True, name="agent", actuation="spd", ): self._name = name self._actuation = SimAgent.Actuation.from_string(actuation) self._pb_client = pybullet_client self._char_info = char_info # Load self._body_id file char_create_flags = self._pb_client.URDF_MAINTAIN_LINK_ORDER if self_collision: char_create_flags = char_create_flags|\ self._pb_client.URDF_USE_SELF_COLLISION|\ self._pb_client.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS self._body_id = self._pb_client.loadURDF(model_file, [0, 0, 0], globalScaling=scale, useFixedBase=False, flags=char_create_flags) for pair in self._char_info.collison_ignore_pairs: self._pb_client.setCollisionFilterPair( self._body_id, self._body_id, pair[0], pair[1], enableCollision=False) # TODO: should ref_scale be removed? self._ref_scale = ref_scale self._num_joint = self._pb_client.getNumJoints(self._body_id) self._joint_indices = range(self._num_joint) self._link_indices = range(-1, self._num_joint) self._joint_indices_movable = [] if kinematic_only: self.setup_kinematics() else: self.setup_dynamics() # Pre-compute informations about the agent self._joint_type = [] self._joint_axis = [] self._joint_dofs = [] for j in self._joint_indices: joint_info = self._pb_client.getJointInfo(self._body_id, j) self._joint_type.append(joint_info[2]) self._joint_axis.append(np.array(joint_info[13])) # if verbose: # print('-----------------------') # print(joint_info[1]) # print('joint_type', joint_info[2]) # print('joint_damping', joint_info[6]) # print('joint_friction', joint_info[7]) # print('joint_upper_limit', joint_info[8]) # print('joint_lower_limit', joint_info[9]) # print('joint_max_force', joint_info[10]) # print('joint_max_vel', joint_info[11]) for j in self._joint_indices: if self._joint_type[j] == self._pb_client.JOINT_SPHERICAL: self._joint_dofs.append(3) self._joint_indices_movable.append(j) elif self._joint_type[j] == self._pb_client.JOINT_REVOLUTE: self._joint_dofs.append(1) self._joint_indices_movable.append(j) elif self._joint_type[j] == self._pb_client.JOINT_FIXED: self._joint_dofs.append(0) else: raise NotImplementedError() self._num_dofs = np.sum(self._joint_dofs) self._joint_pose_init, self._joint_vel_init = self.get_joint_states() self._joint_parent_link = [] self._joint_xform_from_parent_link = [] for j in self._joint_indices: joint_info = self._pb_client.getJointInfo(self._body_id, j) joint_local_p = np.array(joint_info[14]) joint_local_Q = np.array(joint_info[15]) link_idx = joint_info[16] self._joint_parent_link.append(link_idx) self._joint_xform_from_parent_link.append( conversions.Qp2T(joint_local_Q, joint_local_p)) self._link_masses = [] self._link_total_mass = 0.0 for i in self._link_indices: di = self._pb_client.getDynamicsInfo(self._body_id, i) mass = di[0] self._link_total_mass += mass self._link_masses.append(mass) if verbose: print('[SimAgent] Creating an agent...', model_file) print('num_joint <%d>, num_dofs <%d>, total_mass<%f>'\ %(self._num_joint, self._num_dofs, self._link_total_mass)) def get_name(self): return self._name def split_joint_variables(self, states, joint_indices): states_out = [] idx = 0 for j in joint_indices: joint_type = self._joint_type[j] if joint_type == self._pb_client.JOINT_SPHERICAL: Q = conversions.A2Q(np.array(states[idx:idx+3])) states_out.append(Q) idx += 3 elif joint_type == self._pb_client.JOINT_REVOLUTE: states_out.append([states[idx]]) idx += 1 elif joint_type == self._pb_client.JOINT_FIXED: pass else: raise NotImplementedError() assert idx == len(states) return states_out def setup_dynamics(self): # Settings for the simulation self._body_id for j in self._link_indices: self._pb_client.changeDynamics(self._body_id, j, lateralFriction=self._char_info.friction_lateral, spinningFriction=self._char_info.friction_spinning, jointDamping=0.0, restitution=self._char_info.restitution) di = self._pb_client.getDynamicsInfo(self._body_id, j) self._pb_client.changeDynamics(self._body_id, -1, linearDamping=0, angularDamping=0) # Disable the initial motor control for j in self._joint_indices: self._pb_client.setJointMotorControl2(self._body_id, j, self._pb_client.POSITION_CONTROL, targetVelocity=0, force=0) self._pb_client.setJointMotorControlMultiDof(self._body_id, j, self._pb_client.POSITION_CONTROL, targetPosition=[0,0,0,1], targetVelocity=[0,0,0], positionGain=0, velocityGain=1, force=[0,0,0]) for j in self._joint_indices: self._pb_client.enableJointForceTorqueSensor(self._body_id, j, enableSensor=True) def setup_kinematics(self): # Settings for the kinematic self._body_id so that it does not affect the simulation self._pb_client.changeDynamics(self._body_id, -1, linearDamping=0, angularDamping=0) self._pb_client.setCollisionFilterGroupMask(self._body_id, -1, collisionFilterGroup=0, collisionFilterMask=0) for j in range(-1, self._pb_client.getNumJoints(self._body_id)): self._pb_client.setCollisionFilterGroupMask(self._body_id, j, collisionFilterGroup=0, collisionFilterMask=0) self._pb_client.changeDynamics( self._body_id, j, activationState=self._pb_client.ACTIVATION_STATE_SLEEP + self._pb_client.ACTIVATION_STATE_ENABLE_SLEEPING + self._pb_client.ACTIVATION_STATE_DISABLE_WAKEUP) # self._pb_client.changeVisualShape(self._body_id, j, rgbaColor=[1, 1, 1, 0.4]) def change_visual_color(self, color): for j in range(self._pb_client.getNumJoints(self._body_id)): self._pb_client.changeVisualShape(self._body_id, j, rgbaColor=color) def get_num_dofs(self): return self._num_dofs def get_num_joint(self): return self._num_joint def get_joint_type(self, idx): return self._joint_type[idx] def get_joint_axis(self, idx): return self._joint_axis[idx] def get_joint_dofs(self, idx): return self._joint_dofs[idx] def get_root_height_from_ground(self, ground_height=0.0): p, _, _, _ = bu.get_base_pQvw(self._pb_client, self._body_id) vec_root_from_ground = math.projectionOnVector(p, self._char_info.v_up_env) return np.linalg.norm(vec_root_from_ground) - ground_height def get_root_state(self): return bu.get_base_pQvw(self._pb_client, self._body_id) def get_root_transform(self): p, Q, _, _ = bu.get_base_pQvw(self._pb_client, self._body_id) return conversions.Qp2T(Q, p) def set_root_transform(self, T): Q, p = conversions.T2Qp(T) bu.set_base_pQvw(self._pb_client, self._body_id, p, Q, None, None) def get_facing_transform(self, ground_height=0.0): d, p = self.get_facing_direction_position(ground_height) z = d y = self._char_info.v_up_env x = np.cross(y, z) return conversions.Rp2T(np.array([x, y, z]).transpose(), p) def get_facing_position(self, ground_height=0.0): d, p = self.get_facing_direction_position(ground_height) return p def get_facing_direction(self): d, p = self.get_facing_direction_position() return d def get_facing_direction_position(self, ground_height=0.0): R, p = conversions.T2Rp(self.get_root_transform()) d = np.dot(R, self._char_info.v_face) if np.allclose(d, self._char_info.v_up_env): msg = \ '\n+++++++++++++++++WARNING+++++++++++++++++++\n'+\ 'The facing direction is ill-defined ' +\ '(i.e. parellel to the world up-vector).\n'+\ 'A random direction will be assigned for the direction\n'+\ 'Be careful if your system is sensitive to the facing direction\n'+\ '+++++++++++++++++++++++++++++++++++++++++++\n' warnings.warn(msg) d = math.random_unit_vector() d = d - math.projectionOnVector(d, self._char_info.v_up_env) p = p - math.projectionOnVector(p, self._char_info.v_up_env) if ground_height != 0.0: p += ground_height * self._char_info.v_up_env return d/np.linalg.norm(d), p def project_to_ground(self, v): return v - math.projectionOnVector(v, self._char_info.v_up_env) def get_link_states(self, indices=None): return bu.get_link_pQvw(self._pb_client, self._body_id, indices) def get_joint_states(self, indices=None): return bu.get_joint_pv(self._pb_client, self._body_id, indices) def set_pose_by_xform(self, xform): assert len(xform) == len(self._char_info.bvh_map_inv) ''' Base ''' Q, p = conversions.T2Qp(xform[0]) p *= self._ref_scale bu.set_base_pQvw(self._pb_client, self._body_id, p, Q, None, None) ''' Others ''' indices = [] state_pos = [] state_vel = [] idx = -1 for k, j in self._char_info.bvh_map_inv.items(): idx += 1 if idx == 0: continue if j is None: continue joint_type = self._joint_type[j] if joint_type == self._pb_client.JOINT_FIXED: continue T = xform[idx] if joint_type == self._pb_client.JOINT_SPHERICAL: Q, p = conversions.T2Qp(T) w = np.zeros(3) state_pos.append(Q) state_vel.append(w) elif joint_type == self._pb_client.JOINT_REVOLUTE: joint_axis = self.get_joint_axis(j) R, p = conversions.T2Rp(T) w = np.zeros(3) state_pos.append(math.project_rotation_1D(R, joint_axis)) state_vel.append(math.project_angular_vel_1D(w, joint_axis)) else: raise NotImplementedError() indices.append(j) bu.set_joint_pv(self._pb_client, self._body_id, indices, state_pos, state_vel) def set_pose(self, pose, vel=None): ''' Velocity should be represented w.r.t. local frame ''' # Root joint T = pose.get_transform( self._char_info.bvh_map[self._char_info.ROOT], local=False) Q, p = conversions.T2Qp(T) p *= self._ref_scale v, w = None, None if vel is not None: # Here we give a root orientation to get velocities represeted in world frame. R = conversions.Q2R(Q) w = vel.get_angular( self._char_info.bvh_map[self._char_info.ROOT], False, R) v = vel.get_linear( self._char_info.bvh_map[self._char_info.ROOT], False, R) v *= self._ref_scale bu.set_base_pQvw(self._pb_client, self._body_id, p, Q, v, w) # Other joints indices = [] state_pos = [] state_vel = [] for j in self._joint_indices: joint_type = self._joint_type[j] # When the target joint do not have dof, we simply ignore it if joint_type == self._pb_client.JOINT_FIXED: continue # When there is no matching between the given pose and the simulated character, # the character just tries to hold its initial pose if self._char_info.bvh_map[j] is None: state_pos.append(self._joint_pose_init[j]) state_vel.append(self._joint_vel_init[j]) else: T = pose.get_transform(self._char_info.bvh_map[j], local=True) if joint_type == self._pb_client.JOINT_SPHERICAL: Q, p = conversions.T2Qp(T) w = np.zeros(3) if vel is None else vel.get_angular(self._char_info.bvh_map[j], local=True) state_pos.append(Q) state_vel.append(w) elif joint_type == self._pb_client.JOINT_REVOLUTE: joint_axis = self.get_joint_axis(j) R, p = conversions.T2Rp(T) w = np.zeros(3) if vel is None else vel.get_angular(self._char_info.bvh_map[j], local=True) state_pos.append([math.project_rotation_1D(R, joint_axis)]) state_vel.append([math.project_angular_vel_1D(w, joint_axis)]) else: raise NotImplementedError() indices.append(j) bu.set_joint_pv(self._pb_client, self._body_id, indices, state_pos, state_vel) def get_pose(self, skel): p, Q = self._pb_client.getBasePositionAndOrientation(self._body_id) states = self._pb_client.getJointStatesMultiDof(self._body_id, self._joint_indices) pose_data = [] for i in range(skel.num_joint()): joint = skel.joints[i] if joint == skel.root_joint: pose_data.append(conversions.Qp2T(Q, p)) else: j = self._char_info.bvh_map_inv[joint.name] if j is None: pose_data.append(constants.eye_T()) else: joint_type = self._joint_type[j] if joint_type == self._pb_client.JOINT_FIXED: pose_data.append(constants.eye_T()) elif joint_type == self._pb_client.JOINT_SPHERICAL: pose_data.append(conversions.Q2T(states[j][0])) else: raise NotImplementedError() return motion.Pose(skel, pose_data) def array_to_pose_data(self, skel, data, T_root_ref=None): assert len(data) == self._num_dofs + 6 T_root = conversions.Rp2T(conversions.A2R(data[3:6]), data[0:3]) if T_root_ref is not None: T_root = np.dot(T_root_ref, T_root) pose_data = [] idx = 6 for i in range(skel.num_joint()): joint = skel.joints[i] if joint == skel.root_joint: pose_data.append(T_root) else: j = self._char_info.bvh_map_inv[joint.name] if j is None: pose_data.append(constants.eye_T()) else: joint_type = self._joint_type[j] if joint_type == self._pb_client.JOINT_FIXED: pose_data.append(constants.eye_T()) elif joint_type == self._pb_client.JOINT_SPHERICAL: pose_data.append(conversions.R2T(conversions.A2R(data[idx:idx+3]))) idx += 3 else: raise NotImplementedError() return pose_data def arrary_to_pose(self, skel, data, T_root_ref=None): pose_data = self.array_to_pose_data(skel, data) return motion.Pose(skel, pose_data) def save_states(self): return bu.get_state_all(self._pb_client, self._body_id) def restore_states(self, states): bu.set_state_all(self._pb_client, self._body_id, states) def get_com_and_com_vel(self): return bu.compute_com_and_com_vel(self._pb_client, self._body_id, self._link_indices) def get_joint_torques(self): return bu.get_joint_torques(self._pb_client, self._body_id, self._joint_indices) def get_joint_weights(self, skel): ''' Get joint weight values form char_info ''' joint_weights = [] for j in skel.joints: idx = self._char_info.bvh_map_inv[j.name] if idx is None: joint_weights.append(0.0) else: w = self._char_info.joint_weight[idx] joint_weights.append(w) return np.array(joint_weights) def interaction_mesh_samples(self): assert self._char_info.interaction_mesh_samples is not None def get_joint_position(j, p_root, Q_root, p_link, Q_link): if j==self._char_info.ROOT or self._joint_parent_link[j]==self._char_info.ROOT: p, Q = p_root, Q_root else: p, Q = p_link[self._joint_parent_link[j]], Q_link[self._joint_parent_link[j]] T_link_world = conversions.Qp2T(Q, p) T_joint_local = constants.eye_T() if j==self._char_info.ROOT else self._joint_xform_from_parent_link[j] T_joint_world = np.dot(T_link_world, T_joint_local) return conversions.T2p(T_joint_world) points = [] p_root, Q_root, _, _ = self.get_root_state() p_link, Q_link, _, _ = self.get_link_states() for j1, j2, alpha in self._char_info.interaction_mesh_samples: p1 = get_joint_position(j1, p_root, Q_root, p_link, Q_link) p2 = p1 if j2 is None else get_joint_position(j2, p_root, Q_root, p_link, Q_link) points.append((1.0 - alpha) * p1 + alpha * p2) return points def inverse_kinematics(self, indices, positions): assert len(indices) == len(positions) new_positions = self._pb_client.calculateInverseKinematics2( self._body_id, endEffectorLinkIndices=indices, targetPositions=positions, solver=0, maxNumIterations=100, residualThreshold=.01) # new_positions = self._pb_client.calculateInverseKinematics(self._body_id, self._char_info.RightHand, np.zeros(3)) new_positions = self.split_joint_variables(new_positions, self._joint_indices_movable) for p in new_positions: print(p) self._pb_client.resetJointStatesMultiDof(self._body_id, self._joint_indices_movable, new_positions) def actuate(self, pose=None, vel=None, torque=None): if self._actuation == SimAgent.Actuation.NONE: return joint_indices = [] target_positions = [] target_velocities = [] kps = [] kds = [] max_forces = [] for j in self._joint_indices: joint_type = self.get_joint_type(j) if joint_type == self._pb_client.JOINT_FIXED: ''' Ignore fixed joints ''' continue joint_indices.append(j) if self._actuation==SimAgent.Actuation.TQ: ''' No need to compute target values for torque control ''' continue if self._char_info.bvh_map[j] == None: ''' Use the initial pose if no mapping exists ''' target_pos = self._joint_pose_init[j] target_vel = self._joint_vel_init[j] else: ''' Convert target pose value so that it fits to each joint type For the hinge joint, we find the geodesic closest value given the axis For the ''' if pose is None: T = constants.eye_T() else: T = pose.get_transform(self._char_info.bvh_map[j], local=True) if vel is None: w = np.zeros(3) else: w = vel.get_angular(self._char_info.bvh_map[j]) if joint_type == self._pb_client.JOINT_REVOLUTE: axis = self.get_joint_axis(j) target_pos = np.array([math.project_rotation_1D(conversions.T2R(T), axis)]) target_vel = np.array([math.project_angular_vel_1D(w, axis)]) max_force = np.array([self._char_info.max_force[j]]) elif joint_type == self._pb_client.JOINT_SPHERICAL: Q, p = conversions.T2Qp(T) Q = quaternion.Q_op(Q, op=["normalize", "halfspace"]) target_pos = Q target_vel = w max_force = np.ones(3) * self._char_info.max_force[j] else: raise NotImplementedError target_positions.append(target_pos) target_velocities.append(target_vel) if self._actuation==SimAgent.Actuation.SPD: kps.append(self._char_info.kp[j]) kds.append(self._char_info.kd[j]) elif self._actuation==SimAgent.Actuation.PD: ''' TODO: remove ''' kps.append(1.5*self._char_info.kp[j]) kds.append(0.01*self._char_info.kd[j]) elif self._actuation==SimAgent.Actuation.CPD or \ self._actuation==SimAgent.Actuation.CP or \ self._actuation==SimAgent.Actuation.V: kps.append(self._char_info.cpd_ratio*self._char_info.kp[j]) kds.append(self._char_info.cpd_ratio*self._char_info.kd[j]) max_forces.append(max_force) if self._actuation==SimAgent.Actuation.SPD: self._pb_client.setJointMotorControlMultiDofArray(self._body_id, joint_indices, self._pb_client.STABLE_PD_CONTROL, targetPositions=target_positions, targetVelocities=target_velocities, forces=max_forces, positionGains=kps, velocityGains=kds) elif self._actuation==SimAgent.Actuation.PD: ''' Standard PD in Bullet does not support spherical joint yet ''' # self._pb_client.setJointMotorControlMultiDofArray(self._body_id, # joint_indices, # self._pb_client.PD_CONTROL, # targetPositions=target_positions, # targetVelocities=target_velocities, # forces=max_forces, # positionGains=kps, # velocityGains=kds) forces = bu.compute_PD_forces(pb_client=self._pb_client, body_id=self._body_id, joint_indices=joint_indices, desired_positions=target_positions, desired_velocities=target_velocities, kps=kps, kds=kds, max_forces=max_forces) self._pb_client.setJointMotorControlMultiDofArray(self._body_id, joint_indices, self._pb_client.TORQUE_CONTROL, forces=forces) elif self._actuation==SimAgent.Actuation.CPD: self._pb_client.setJointMotorControlMultiDofArray(self._body_id, joint_indices, self._pb_client.POSITION_CONTROL, targetPositions=target_positions, targetVelocities=target_velocities, forces=max_forces, positionGains=kps, velocityGains=kds) elif self._actuation==SimAgent.Actuation.CP: self._pb_client.setJointMotorControlMultiDofArray(self._body_id, joint_indices, self._pb_client.POSITION_CONTROL, targetPositions=target_positions, forces=max_forces, positionGains=kps) elif self._actuation==SimAgent.Actuation.V: self._pb_client.setJointMotorControlMultiDofArray(self._body_id, joint_indices, self._pb_client.VELOCITY_CONTROL, targetVelocities=target_velocities, forces=max_forces, velocityGains=kds) elif self._actuation==SimAgent.Actuation.TQ: self._pb_client.setJointMotorControlMultiDofArray(self._body_id, joint_indices, self._pb_client.TORQUE_CONTROL, forces=torque) else: raise NotImplementedError ================================================ FILE: sim_obstacle.py ================================================ # Copyright (c) Facebook, Inc. and its affiliates. from enum import Enum import numpy as np from fairmotion.ops import conversions from fairmotion.ops import math from bullet import bullet_utils as bu import render_module as rm gl_render = None bullet_render = None gl = None def filter_list_by_index(old_list, pos, positive=True): if positive: return [old_list[i] for i, e in enumerate(old_list) if i in pos] else: return [old_list[i] for i, e in enumerate(old_list) if i not in pos] class Shape(Enum): BOX = 1 SPHERE = 2 class Obstacle(object): def __init__(self, name, duration, shape, mass, size, p, Q, v, w): self.name = name self.duration = duration self.mass = mass self.size = size self.shape = shape self.p = p self.Q = Q self.v = v self.w = w self.body_id = None self.color = [0.8, 0.8, 0.8, 1.0] self.lateral_friction = 0.8 self.spinning_friction = 0.5 self.restitution = 0.2 self.linear_damping = 0.0 self.angular_damping = 0.2 self.movable = True class ObstacleManager(object): def __init__(self, pb_client, dt, v_up_env, visualization=False): self.pb_client = pb_client self.obstacles = [] self.dt = dt self.v_up_env = v_up_env if visualization: global gl_render, bullet_render, gl from basecode.render import gl_render from basecode.bullet import bullet_render import OpenGL.GL as gl def clear(self): for obs in self.obstacles: self.pb_client.removeBody(obs.body_id) self.obstacles = [] def launch(self, obstacle): p = self.pb_client size = obstacle.size if obstacle.shape == Shape.BOX: colShapeId = p.createCollisionShape( p.GEOM_BOX, halfExtents=[0.5*size[0], 0.5*size[1], 0.5*size[2]]) elif obstacle.shape == Shape.SPHERE: colShapeId = p.createCollisionShape( p.GEOM_SPHERE, radius=size[0]) else: raise NotImplementedError body_id = p.createMultiBody(baseMass=obstacle.mass, baseCollisionShapeIndex=colShapeId, basePosition=obstacle.p, baseOrientation=obstacle.Q) p.resetBaseVelocity(body_id, obstacle.v, obstacle.w) p.changeDynamics(body_id, -1, lateralFriction=obstacle.lateral_friction, spinningFriction=obstacle.spinning_friction, restitution=obstacle.restitution, linearDamping=obstacle.linear_damping, angularDamping=obstacle.angular_damping ) obstacle.body_id = body_id self.obstacles.append(obstacle) def throw(self, pos_target, num=1, duration=2.0, shape=Shape.BOX, vel=8.0, r_out=2.0, r_in=0.2, mass=2.0, size=0.2*np.ones(3), h_min=0.5): assert r_out > r_in for _ in range(num): d_out = math.random_unit_vector() d_in = math.random_unit_vector() p_from = pos_target + r_out * d_out p_projected_h = math.projectionOnVector(p_from, self.v_up_env) h_cliped = max(np.linalg.norm(p_projected_h), h_min) p_from = (p_from-p_projected_h) + h_cliped * self.v_up_env p_to = pos_target + r_in * d_in v_dir = p_to - p_from v_dir = v_dir / np.linalg.norm(v_dir) p = p_from Q = conversions.A2Q( math.random_unit_vector()*np.random.uniform(-np.pi, np.pi)) v = vel * v_dir w = np.zeros(3) obs = Obstacle("", duration, shape, mass, size, p, Q, v, w) self.launch(obs) def update(self): deleted_idx = [] for i in range(len(self.obstacles)): obs = self.obstacles[i] obs.duration -= self.dt if obs.duration <= 0.0: self.pb_client.removeBody(obs.body_id) deleted_idx.append(i) else: p, Q, v, w = bu.get_base_pQvw(self.pb_client, obs.body_id) obs.p, obs.Q, obs.v, obs.w = p, Q, v, w self.obstacles = filter_list_by_index(self.obstacles, deleted_idx, positive=False) def render(self): for obs in self.obstacles: decay_start = 0.5 alpha = min(1.0, 1.0/decay_start*obs.duration) c = obs.color T = conversions.Qp2T(obs.Q, obs.p) rm.gl.glPushMatrix() rm.gl_render.glTransform(T) if obs.shape == Shape.BOX: geom_type = self.pb_client.GEOM_BOX elif obs.shape == Shape.SPHERE: geom_type = self.pb_client.GEOM_SPHERE else: raise NotImplementedError rm.bullet_render.render_geom(geom_type=geom_type, geom_size=obs.size, color=[c[0],c[1],c[2],alpha]) rm.bullet_render.render_geom_info(geom_type=geom_type, geom_size=obs.size) rm.gl.glPopMatrix() # bullet_render.render_model(self.pb_client, obs.body_id, color=[0.8, 0.8, 0.8, alpha])