Repository: 1g0rrr/SimpleAutomation
Branch: main
Commit: 2bc237ebccdb
Files: 14
Total size: 50.2 KB
Directory structure:
gitextract_ljmww4sq/
├── .gitignore
├── LICENSE
├── README.md
├── colab/
│ └── SimpleAutomationTrainModel.ipynb
└── core/
├── configs/
│ ├── chains/
│ │ ├── chip_testing.yaml
│ │ ├── eat_chips.yaml
│ │ ├── glue_stick.yaml
│ │ └── lamp_testing.yaml
│ └── robot/
│ ├── koch.yaml
│ ├── so100.yaml
│ └── so100_dummy.yaml
├── dummy_arm.py
├── models_chain.py
└── test.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
# Logging
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# mypy
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# Cython debug symbols
cython_debug/
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# Make robots do something useful!
Our goal is to make robots affordable so more people can try them out, discover useful applications, and eventually make money using them to do work.
Currently, it's a set of helper scripts on top of LeRobot, plus a $300 robot arm compatible with π₀ and other foundational robotics models.
# Intro
# Installation
1. If you didn't install LeRobot, install it:
```
git clone https://github.com/huggingface/lerobot.git
cd lerobot
pip install -e .
```
2 Clone Simple Automation scripts to another folder
```
git clone https://github.com/1g0rrr/SimpleAutomation.git
cd SimpleAutomation
```
3 Setup ports for your robot in "core/configs/robot/so100.yaml".
# Run
### Use multiple ACT models to solve complex robotics tasks.
For example, in the lamp testing demo, we combined 3 models:
1. For getting the lamp from a random position
2. For precise insertion into the tester
3. For sorting working/not working bulbs
### Run evaluation
- Change the config file for using your models "core/configs/chains/lamp_testing.yaml"
- While evaluating press "right" key to move to the next model
```
python core/models_chain.py evaluate \
--robot-path core/configs/robot/so100.yaml \
--chain-path core/configs/chains/lamp_testing.yaml
```
### Use LLM agent to run models
```
python core/models_chain.py llm_agent \
--robot-path core/configs/robot/so100.yaml
```
### Run recording
- The difference from Lerobot's recording is added teleoperation between episodes. This is usefull to be able to switch between models in not "resting" position.
```
python core/models_chain.py record \
--robot-path core/configs/robot/so100.yaml \
--fps 30 \
--root data \
--repo-id 1g0rrr/koch_test21 \
--tags tutorial \
--warmup-time-s 5 \
--episode-time-s 5 \
--reset-time-s 5 \
--num-episodes 2
```
### Run teleoperation:
Use it for testing if all is working.
```
python core/models_chain.py teleoperate \
--robot-path core/configs/robot/so100.yaml \
--robot-overrides '~cameras'
```
# Tips
- Make sure you have all inintial positions in the following model to prevent robot from sudden movements.
- "Pick and place" task is hard for the model and gripper can grab object not precisely at the center. To solve this re-grab object at the beginning of next model.
# Training
### Train model in Google Colab:
You can model in Google Colab to save time.
https://colab.research.google.com/github/1g0rrr/SimpleAutomation/blob/main/colab/SimpleAutomationTrainModel.ipynb
- It will take about 2.5 hours and $1.5 to train typical 80K steps.
- Choose A100 as the fastest GPU.
- Don't disconnect colab and don't close browser as all data will be deleted.
# Hardware
### S.A.M.01
Join https://discord.gg/NFsqq4CVhs to get recent information.
### SO-100
Follow [SO-100](https://github.com/TheRobotStudio/SO-ARM100) to build your arm.
# Join the community
Say 👋 in our [public discord channel](https://discord.gg/NFsqq4CVhs). We help each other with assembling, training models, and making the robot do something useful.
Thank you for contributing to SimpleAutomation!
## Star History
[](https://star-history.com/#1g0rrr/simpleautomation&Timeline)
================================================
FILE: colab/SimpleAutomationTrainModel.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cX-BT9Wte6tV"
},
"outputs": [],
"source": [
"hugging_face_token = ''\n",
"dataset_repo_id = 'test/test-dataset'\n",
"model_repo_id = 'test/test-model'\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bV6yVHzTq-dt"
},
"outputs": [],
"source": [
"!git clone https://github.com/huggingface/lerobot.git\n",
"!cd lerobot && pip install -e . --ignore-installed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4vccJQqd_afv"
},
"outputs": [],
"source": [
"!pip install huggingface-hub\n",
"!huggingface-cli login --token {hugging_face_token} --add-to-git-credential"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EN9GQ7AurBFo"
},
"outputs": [],
"source": [
"!cd lerobot && python lerobot/scripts/train.py \\\n",
" dataset_repo_id={dataset_repo_id} \\\n",
" policy=act_koch_real \\\n",
" env=koch_real \\\n",
" hydra.run.dir=outputs/train/mymodel \\\n",
" hydra.job.name=mymodel \\\n",
" device=cuda \\\n",
" wandb.enable=false\n",
"\n",
"\n",
"!huggingface-cli upload {model_repo_id} lerobot/outputs/train/mymodel/checkpoints/last/pretrained_model\n",
"\n",
"from google.colab import runtime\n",
"runtime.unassign()"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "A100",
"machine_shape": "hm",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: core/configs/chains/chip_testing.yaml
================================================
models:
eat_chips:
repo_id: "1g0rrr/eat_chips"
control_time_s: 120
================================================
FILE: core/configs/chains/eat_chips.yaml
================================================
llm_agent:
model: "gpt-4o-mini"
system_prompt: "You're a helpful robot-arm assistant. Answer simple, fun and super concise."
tools:
eat_chips:
repo_id: "1g0rrr/eat_chips"
desc: "Grab opne chip and give it to user."
grab_candy:
repo_id: "1g0rrr/grab_candy"
desc: "Grab candy and give it to user."
grab_sponge:
repo_id: "1g0rrr/grab_sponge"
desc: "Clean the desktop."
describe_area:
desc: "Describe the area around you."
prompt: "Describe in one phrase what objects you see on the table. Not including robot. Start answer with "I see...""
================================================
FILE: core/configs/chains/glue_stick.yaml
================================================
models:
grab_stick:
repo_id: "1g0rrr/grab_stick"
control_time_s: 120
pause:
control_time_s: 120
release_stick:
repo_id: "1g0rrr/release_stick"
control_time_s: 120
================================================
FILE: core/configs/chains/lamp_testing.yaml
================================================
models:
grab_orange:
repo_id: "1g0rrr/grab_sponge"
control_time_s: 9999
# grab_candy:
# repo_id: "1g0rrr/grab_candy"
# control_time_s: 120
# grab_sponge:
# repo_id: "1g0rrr/grab_sponge"
# control_time_s: 120
# insert_lamp:
# repo_id: "1g0rrr/insert_lamp"
# control_time_s: 120
# testing_lamp:
# repo_id: "1g0rrr/testing_lamp"
# control_time_s: 120
================================================
FILE: core/configs/robot/koch.yaml
================================================
_target_: lerobot.common.robot_devices.robots.manipulator.ManipulatorRobot
robot_type: koch
calibration_dir: .cache/calibration/koch
# `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
# Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
# the number of motors in your follower arms.
max_relative_target: null
leader_arms:
main:
_target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
port: /dev/tty.usbmodem58760433151
motors:
# name: (index, model)
shoulder_pan: [1, "xl330-m077"]
shoulder_lift: [2, "xl330-m077"]
elbow_flex: [3, "xl330-m077"]
wrist_flex: [4, "xl330-m077"]
wrist_roll: [5, "xl330-m077"]
gripper: [6, "xl330-m077"]
follower_arms:
main:
_target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
port: /dev/tty.usbmodem58760434751
motors:
# name: (index, model)
shoulder_pan: [1, "xl430-w250"]
shoulder_lift: [2, "xl430-w250"]
elbow_flex: [3, "xl330-m288"]
wrist_flex: [4, "xl330-m288"]
wrist_roll: [5, "xl330-m288"]
gripper: [6, "xl330-m288"]
cameras:
laptop:
_target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
camera_index: 0
fps: 30
width: 640
height: 480
phone:
_target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
camera_index: 1
fps: 30
width: 640
height: 480
# ~ Koch specific settings ~
# Sets the leader arm in torque mode with the gripper motor set to this angle. This makes it possible
# to squeeze the gripper and have it spring back to an open position on its own.
gripper_open_degree: 35.156
================================================
FILE: core/configs/robot/so100.yaml
================================================
_target_: lerobot.common.robot_devices.robots.manipulator.ManipulatorRobot
robot_type: so100
calibration_dir: .cache/calibration/so_100
# `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
# Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
# the number of motors in your follower arms.
max_relative_target: null
# usbmodem58760434751
# dev/tty.usbmodem58760434751 - leader with original bus
leader_arms:
main:
_target_: lerobot.common.robot_devices.motors.feetech.FeetechMotorsBus
# _target_: dummy_arm.SimulatedFollower
port: /dev/tty.usbmodem58760434771
motors:
# name: (index, model)
shoulder_pan: [1, "sts3215"]
shoulder_lift: [2, "sts3215"]
elbow_flex: [3, "sts3215"]
wrist_flex: [4, "sts3215"]
wrist_roll: [5, "sts3215"]
gripper: [6, "sts3215"]
follower_arms:
main:
_target_: lerobot.common.robot_devices.motors.feetech.FeetechMotorsBus
# _target_: dummy_arm.SimulatedFollower
port: /dev/tty.usbmodem58760429711
motors:
# name: (index, model)
shoulder_pan: [1, "sts3215"]
shoulder_lift: [2, "sts3215"]
elbow_flex: [3, "sts3215"]
wrist_flex: [4, "sts3215"]
wrist_roll: [5, "sts3215"]
gripper: [6, "sts3215"]
cameras:
laptop:
_target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
camera_index: 0
fps: 30
width: 640
height: 480
phone:
_target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
camera_index: 1
fps: 30
width: 640
height: 480
# ~ Koch specific settings ~
# Sets the leader arm in torque mode with the gripper motor set to this angle. This makes it possible
# to squeeze the gripper and have it spring back to an open position on its own.
# gripper_open_degree: 35.156
================================================
FILE: core/configs/robot/so100_dummy.yaml
================================================
_target_: lerobot.common.robot_devices.robots.manipulator.ManipulatorRobot
robot_type: so100
calibration_dir: .cache/calibration/so_100
# `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
# Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
# the number of motors in your follower arms.
max_relative_target: null
# usbmodem58760434751
# dev/tty.usbmodem58760434751 - leader with original bus
leader_arms:
main:
# _target_: lerobot.common.robot_devices.motors.feetech.FeetechMotorsBus
_target_: dummy_arm.SimulatedFollower
port: /dev/tty.usbmodem58760434771
motors:
# name: (index, model)
shoulder_pan: [1, "sts3215"]
shoulder_lift: [2, "sts3215"]
elbow_flex: [3, "sts3215"]
wrist_flex: [4, "sts3215"]
wrist_roll: [5, "sts3215"]
gripper: [6, "sts3215"]
follower_arms:
main:
# _target_: lerobot.common.robot_devices.motors.feetech.FeetechMotorsBus
_target_: dummy_arm.SimulatedFollower
port: /dev/tty.usbmodem58760429711
motors:
# name: (index, model)
shoulder_pan: [1, "sts3215"]
shoulder_lift: [2, "sts3215"]
elbow_flex: [3, "sts3215"]
wrist_flex: [4, "sts3215"]
wrist_roll: [5, "sts3215"]
gripper: [6, "sts3215"]
cameras:
laptop:
_target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
camera_index: 0
fps: 30
width: 640
height: 480
phone:
_target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
camera_index: 1
fps: 30
width: 640
height: 480
# ~ Koch specific settings ~
# Sets the leader arm in torque mode with the gripper motor set to this angle. This makes it possible
# to squeeze the gripper and have it spring back to an open position on its own.
# gripper_open_degree: 35.156
================================================
FILE: core/dummy_arm.py
================================================
import enum
import logging
import math
import time
import traceback
from copy import deepcopy
from pathlib import Path
import numpy as np
import tqdm
import mujoco
from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
from lerobot.common.utils.utils import capture_timestamp_utc
class SimulatedFollower:
def __init__(
self,
port: str,
# configuration,
motors: dict[str, tuple[int, str]],
extra_model_control_table: dict[str, list[tuple]] | None = None,
extra_model_resolution: dict[str, int] | None = None,
mock=False,
):
# self.configuration = configuration
self.old_pos = np.zeros(12)
self.port = port
self.motors = {
# name: (index, model)
"shoulder_pan": (1, "xl330-m077"),
"shoulder_lift": (2, "xl330-m077"),
"elbow_flex": (3, "xl330-m077"),
"wrist_flex": (4, "xl330-m077"),
"wrist_roll": (5, "xl330-m077"),
"gripper": (6, "xl330-m077"),
}
pass
@property
def motor_names(self) -> list[str]:
return list(self.motors.keys())
def connect(self):
self.is_connected = True
# self.data = self.configuration.data
# self.model = self.configuration.model
# init_pos_rad = [-1.5708, -1.5708, 1.5708, -1.5708, -1.5708, 0]
# self.data.qpos[-6:] = init_pos_rad
# self.old_pos = deepcopy(self.data.qpos[-6:])
# deep copy
# mujoco.mj_forward(self.model, self.data)
pass
def read(self, data_name, motor_names: str | list[str] | None = None):
values = np.zeros(6)
values = values.astype(np.int32)
return values
def set_calibration(self, calibration: dict[str, tuple[int, bool]]):
self.calibration = calibration
def write(self, data_name, values: int | float | np.ndarray, motor_names: str | list[str] | None = None):
if data_name in ["Torque_Enable", "Operating_Mode", "Homing_Offset", "Drive_Mode", "Position_P_Gain", "Position_I_Gain", "Position_D_Gain"]:
return np.array(None)
pass
def disconnect(self):
self.is_connected = False
def __del__(self):
if getattr(self, "is_connected", False):
self.disconnect()
if __name__ == "__main__":
pass
================================================
FILE: core/models_chain.py
================================================
import argparse
import logging
import time
from pathlib import Path
from typing import List
import os
from dotenv import load_dotenv, find_dotenv
import speech_recognition as sr
from langchain_openai import ChatOpenAI
import shutil
import tqdm
from langchain_core.tools import tool
from langchain.agents import create_tool_calling_agent, AgentExecutor
from lerobot.common.robot_devices.cameras.opencv import OpenCVCamera
# from safetensors.torch import load_file, save_file
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
from lerobot.common.datasets.populate_dataset import (
create_lerobot_dataset,
delete_current_episode,
init_dataset,
save_current_episode,
)
from lerobot.common.robot_devices.control_utils import (
control_loop,
has_method,
init_keyboard_listener,
init_policy,
log_control_info,
record_episode,
reset_environment,
sanity_check_dataset_name,
stop_recording,
warmup_record,
)
from lerobot.common.robot_devices.robots.factory import make_robot
from lerobot.common.robot_devices.robots.utils import Robot
from lerobot.common.robot_devices.utils import busy_wait, safe_disconnect
from lerobot.common.utils.utils import init_hydra_config, init_logging, log_say, none_or_int
########################################################################################
# Control modes
########################################################################################
@safe_disconnect
def calibrate(robot: Robot, arms: list[str] | None):
# TODO(aliberts): move this code in robots' classes
if robot.robot_type.startswith("stretch"):
if not robot.is_connected:
robot.connect()
if not robot.is_homed():
robot.home()
return
unknown_arms = [arm_id for arm_id in arms if arm_id not in robot.available_arms]
available_arms_str = " ".join(robot.available_arms)
unknown_arms_str = " ".join(unknown_arms)
if arms is None or len(arms) == 0:
raise ValueError(
"No arm provided. Use `--arms` as argument with one or more available arms.\n"
f"For instance, to recalibrate all arms add: `--arms {available_arms_str}`"
)
if len(unknown_arms) > 0:
raise ValueError(
f"Unknown arms provided ('{unknown_arms_str}'). Available arms are `{available_arms_str}`."
)
for arm_id in arms:
arm_calib_path = robot.calibration_dir / f"{arm_id}.json"
if arm_calib_path.exists():
print(f"Removing '{arm_calib_path}'")
arm_calib_path.unlink()
else:
print(f"Calibration file not found '{arm_calib_path}'")
if robot.is_connected:
robot.disconnect()
# Calling `connect` automatically runs calibration
# when the calibration file is missing
robot.connect()
robot.disconnect()
print("Calibration is done! You can now teleoperate and record datasets!")
@safe_disconnect
def teleoperate(
robot: Robot, fps: int | None = None, teleop_time_s: float | None = None, display_cameras: bool = False
):
control_loop(
robot,
control_time_s=teleop_time_s,
fps=fps,
teleoperate=True,
display_cameras=display_cameras,
)
@safe_disconnect
def llm_agent(
robot: Robot,
chain_path: str | None = None,
fps: int | None = None,
teleop_time_s: float | None = None,
display_cameras: bool = True
):
import pyttsx3
import base64
import cv2
from langchain.schema import SystemMessage
from langchain_core.prompts import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
)
_ = load_dotenv(find_dotenv())
api_key = os.getenv("OPENAI_API_KEY")
robot.connect()
engine = pyttsx3.init()
models = {
"grab_sponge": {"repo_id": "1g0rrr/grab_sponge", "control_time_s": 32},
"grab_orange": {"repo_id": "1g0rrr/grab_orange", "control_time_s": 10},
"grab_candy":{"repo_id": "1g0rrr/grab_candy", "control_time_s": 10}
}
global policies
policies = {}
for model_name in models:
model = models[model_name]
policy_overrides = ["device=cpu"]
policy, policy_fps, device, use_amp = init_policy(model["repo_id"], policy_overrides)
policies[model_name] = ({"policy": policy, "policy_fps": policy_fps, "device": device, "use_amp": use_amp, "control_time_s": model["control_time_s"]})
@tool(return_direct=True)
def grab_sponge():
"""Clean the desktop.
"""
global policies
do_control_loop(policies["grab_sponge"])
return "Done"
@tool(return_direct=True)
def grab_orange():
"""Grab orange and give it to user.
"""
global policies
do_control_loop(policies["grab_orange"])
return "Done"
@tool(return_direct=True)
def grab_candy():
"""Grab candy and give it to user.
"""
global policies
do_control_loop(policies["grab_candy"])
return "Done"
@tool(return_direct=True)
def describe_area():
"""Describing what I can see.
"""
llm = ChatOpenAI(temperature=0.1, model=llm_model, api_key=api_key)
cam1 = OpenCVCamera(camera_index=0, fps=30, width=640, height=480, color_mode="bgr")
cam1.connect()
img = cam1.read()
# cv2.imshow("Image", img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
_, encoded_img = cv2.imencode('.png', img)
base64_img = base64.b64encode(encoded_img).decode("utf-8")
mime_type = 'image/png'
encoded_image_url = f"data:{mime_type};base64,{base64_img}"
chat_prompt_template = ChatPromptTemplate.from_messages(
messages=[
SystemMessage(content='Describe in one phrase what objects you see on the table. Not including robot. Start answer with "I see..."'),
HumanMessagePromptTemplate.from_template(
[{'image_url': "{encoded_image_url}", 'type': 'image_url'}],
)
]
)
chain = chat_prompt_template | llm
res = chain.invoke({"encoded_image_url": encoded_image_url})
return res.content
def do_control_loop(policy_obj):
global policies, models
control_loop(
robot=robot,
control_time_s=policy_obj["control_time_s"],
display_cameras=display_cameras,
policy=policy_obj["policy"],
device=policy_obj["device"],
use_amp=policy_obj["use_amp"],
fps = policy_obj["policy_fps"],
teleoperate=False,
)
agent_prompt = ChatPromptTemplate.from_messages([
("system", "You're a helpful robot-arm assistant. Answer super concise."),
("human", "{input}"),
("placeholder", "{agent_scratchpad}"),
])
llm_model = "gpt-4o-mini"
llm = ChatOpenAI(temperature=0.1, model=llm_model, api_key=api_key)
tools = [grab_sponge, grab_orange, grab_candy, describe_area]
agent = create_tool_calling_agent(llm, tools, agent_prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
r = sr.Recognizer()
def listen():
with sr.Microphone() as source:
audio = r.listen(source)
print("Processing...")
try:
text = r.recognize_google(audio)
return text
except Exception as e:
print("Error: " + str(e))
return None
def generate_response(prompt):
completions = agent_executor.invoke({"input": prompt, })
message = completions["output"]
return message
while True:
print("Listening...")
audio_prompt = listen()
if audio_prompt is not None:
print("You: " + audio_prompt)
response = generate_response(audio_prompt)
engine.say(response)
engine.runAndWait()
print("Robot: " + response)
# to lower case
if audio_prompt.lower() == "thank you":
# Exit the program
exit()
else:
print("Can you repeat?")
continue
@safe_disconnect
def evaluate(
robot: Robot,
chain_path: str | None = None,
fps: int | None = None,
teleop_time_s: float | None = None,
display_cameras: bool = True
):
robot_cfg = init_hydra_config(chain_path)
models = robot_cfg["models"]
policies = []
for model_name in models:
model = models[model_name]
policy_overrides = ["device=cpu"]
policy, policy_fps, device, use_amp = init_policy(model["repo_id"], policy_overrides)
policies.append({"policy": policy, "policy_fps": policy_fps, "device": device, "use_amp": use_amp, "control_time_s": model["control_time_s"]})
listener, events = init_keyboard_listener()
for policy_obj in policies:
control_loop(
robot=robot,
control_time_s=policy_obj["control_time_s"],
display_cameras=display_cameras,
events=events,
policy=policy_obj["policy"],
device=policy_obj["device"],
use_amp=policy_obj["use_amp"],
fps = policy_obj["policy_fps"],
teleoperate=False,
)
print("Model is done!")
print("Teleoperation is done!")
@safe_disconnect
def record(
robot: Robot,
root: str,
repo_id: str,
pretrained_policy_name_or_path: str | None = None,
policy_overrides: List[str] | None = None,
fps: int | None = None,
warmup_time_s=2,
episode_time_s=10,
reset_time_s=5,
num_episodes=50,
video=True,
run_compute_stats=True,
push_to_hub=True,
tags=None,
num_image_writer_processes=1,
num_image_writer_threads_per_camera=1,
force_override=False,
display_cameras=True,
play_sounds=True,
):
# TODO(rcadene): Add option to record logs
listener = None
events = None
policy = None
device = None
use_amp = None
# Load pretrained policy
if pretrained_policy_name_or_path is not None:
policy, policy_fps, device, use_amp = init_policy(pretrained_policy_name_or_path, policy_overrides)
if fps is None:
fps = policy_fps
logging.warning(f"No fps provided, so using the fps from policy config ({policy_fps}).")
elif fps != policy_fps:
logging.warning(
f"There is a mismatch between the provided fps ({fps}) and the one from policy config ({policy_fps})."
)
# Create empty dataset or load existing saved episodes
sanity_check_dataset_name(repo_id, policy)
dataset = init_dataset(
repo_id,
root,
force_override,
fps,
video,
write_images=robot.has_camera,
num_image_writer_processes=num_image_writer_processes,
num_image_writer_threads=num_image_writer_threads_per_camera * robot.num_cameras,
)
if not robot.is_connected:
robot.connect()
listener, events = init_keyboard_listener()
# Execute a few seconds without recording to:
# 1. teleoperate the robot to move it in starting position if no policy provided,
# 2. give times to the robot devices to connect and start synchronizing,
# 3. place the cameras windows on screen
enable_teleoperation = policy is None
log_say("Warmup record", play_sounds)
warmup_record(robot, events, enable_teleoperation, warmup_time_s, display_cameras, fps)
if has_method(robot, "teleop_safety_stop"):
robot.teleop_safety_stop()
while True:
if dataset["num_episodes"] >= num_episodes:
break
episode_index = dataset["num_episodes"]
# Visual sign in terminal that a recording is starting
print("============================================")
print("============================================")
print("=========== START RECORDING ==============")
print("============================================")
print("============================================")
print("============================================")
log_say(f"Recording episode {episode_index}", play_sounds)
record_episode(
dataset=dataset,
robot=robot,
events=events,
episode_time_s=episode_time_s,
display_cameras=display_cameras,
policy=policy,
device=device,
use_amp=use_amp,
fps=fps,
)
# Execute a few seconds without recording to give time to manually reset the environment
# Current code logic doesn't allow to teleoperate during this time.
# TODO(rcadene): add an option to enable teleoperation during reset
# Skip reset for the last episode to be recorded
if not events["stop_recording"] and (
(episode_index < num_episodes - 1) or events["rerecord_episode"]
):
log_say("Reset the environment", play_sounds)
reset_environment(robot, events, reset_time_s)
# log_say("Prepare position", play_sounds)
# warmup_record(robot, events, enable_teleoperation, warmup_time_s, display_cameras, fps)
if events["rerecord_episode"]:
log_say("Re-record episode", play_sounds)
events["rerecord_episode"] = False
events["exit_early"] = False
delete_current_episode(dataset)
continue
# Increment by one dataset["current_episode_index"]
save_current_episode(dataset)
if events["stop_recording"]:
break
log_say("Stop recording", play_sounds, blocking=True)
stop_recording(robot, listener, display_cameras)
lerobot_dataset = create_lerobot_dataset(dataset, run_compute_stats, push_to_hub, tags, play_sounds)
data_dict = ["observation.images.laptop", "observation.images.phone"]
image_keys = [key for key in data_dict if "image" in key]
local_dir = Path(root) / repo_id
videos_dir = local_dir / "videos"
for episode_index in tqdm.tqdm(range(num_episodes)):
for key in image_keys:
# key = f"observation.images.{name}"
tmp_imgs_dir = videos_dir / f"{key}_episode_{episode_index:06d}"
shutil.rmtree(tmp_imgs_dir, ignore_errors=True)
log_say("Exiting", play_sounds)
return lerobot_dataset
@safe_disconnect
def replay(
robot: Robot, episode: int, fps: int | None = None, root="data", repo_id="lerobot/debug", play_sounds=True
):
# TODO(rcadene, aliberts): refactor with control_loop, once `dataset` is an instance of LeRobotDataset
# TODO(rcadene): Add option to record logs
local_dir = Path(root) / repo_id
if not local_dir.exists():
raise ValueError(local_dir)
dataset = LeRobotDataset(repo_id, root=root)
items = dataset.hf_dataset.select_columns("action")
from_idx = dataset.episode_data_index["from"][episode].item()
to_idx = dataset.episode_data_index["to"][episode].item()
if not robot.is_connected:
robot.connect()
log_say("Replaying episode", play_sounds, blocking=True)
for idx in range(from_idx, to_idx):
start_episode_t = time.perf_counter()
action = items[idx]["action"]
robot.send_action(action)
dt_s = time.perf_counter() - start_episode_t
busy_wait(1 / fps - dt_s)
dt_s = time.perf_counter() - start_episode_t
log_control_info(robot, dt_s, fps=fps)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers(dest="mode", required=True)
# Set common options for all the subparsers
base_parser = argparse.ArgumentParser(add_help=False)
base_parser.add_argument(
"--robot-path",
type=str,
default="lerobot/configs/robot/koch.yaml",
help="Path to robot yaml file used to instantiate the robot using `make_robot` factory function.",
)
base_parser.add_argument(
"--robot-overrides",
type=str,
nargs="*",
help="Any key=value arguments to override config values (use dots for.nested=overrides)",
)
parser_calib = subparsers.add_parser("calibrate", parents=[base_parser])
parser_calib.add_argument(
"--arms",
type=str,
nargs="*",
help="List of arms to calibrate (e.g. `--arms left_follower right_follower left_leader`)",
)
parser_teleop = subparsers.add_parser("teleoperate", parents=[base_parser])
parser_teleop.add_argument(
"--fps", type=none_or_int, default=None, help="Frames per second (set to None to disable)"
)
parser_teleop.add_argument(
"--display-cameras",
type=int,
default=1,
help="Display all cameras on screen (set to 1 to display or 0).",
)
parser_evaluate = subparsers.add_parser("evaluate", parents=[base_parser])
parser_evaluate.add_argument(
"--fps", type=none_or_int, default=None, help="Frames per second (set to None to disable)"
)
parser_evaluate.add_argument(
"--chain-path",
type=Path,
default="core/configs/chains/lamp_testing.yaml",
help="Path to chain configuration yaml file').",
)
parser_llm_agent = subparsers.add_parser("llm_agent", parents=[base_parser])
parser_llm_agent.add_argument(
"--chain-path",
type=Path,
default="core/configs/chains/clean_whiteboard.yaml",
help="Path to chain configuration yaml file').",
)
parser_record = subparsers.add_parser("record", parents=[base_parser])
parser_record.add_argument(
"--fps", type=none_or_int, default=None, help="Frames per second (set to None to disable)"
)
parser_record.add_argument(
"--root",
type=Path,
default="data",
help="Root directory where the dataset will be stored locally at '{root}/{repo_id}' (e.g. 'data/hf_username/dataset_name').",
)
parser_record.add_argument(
"--repo-id",
type=str,
default="lerobot/test",
help="Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).",
)
parser_record.add_argument(
"--warmup-time-s",
type=int,
default=10,
help="Number of seconds before starting data collection. It allows the robot devices to warmup and synchronize.",
)
parser_record.add_argument(
"--episode-time-s",
type=int,
default=60,
help="Number of seconds for data recording for each episode.",
)
parser_record.add_argument(
"--reset-time-s",
type=int,
default=60,
help="Number of seconds for resetting the environment after each episode.",
)
parser_record.add_argument("--num-episodes", type=int, default=50, help="Number of episodes to record.")
parser_record.add_argument(
"--run-compute-stats",
type=int,
default=1,
help="By default, run the computation of the data statistics at the end of data collection. Compute intensive and not required to just replay an episode.",
)
parser_record.add_argument(
"--push-to-hub",
type=int,
default=1,
help="Upload dataset to Hugging Face hub.",
)
parser_record.add_argument(
"--tags",
type=str,
nargs="*",
help="Add tags to your dataset on the hub.",
)
parser_record.add_argument(
"--num-image-writer-processes",
type=int,
default=0,
help=(
"Number of subprocesses handling the saving of frames as PNGs. Set to 0 to use threads only; "
"set to ≥1 to use subprocesses, each using threads to write images. The best number of processes "
"and threads depends on your system. We recommend 4 threads per camera with 0 processes. "
"If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses."
),
)
parser_record.add_argument(
"--num-image-writer-threads-per-camera",
type=int,
default=8,
help=(
"Number of threads writing the frames as png images on disk, per camera. "
"Too many threads might cause unstable teleoperation fps due to main thread being blocked. "
"Not enough threads might cause low camera fps."
),
)
parser_record.add_argument(
"--force-override",
type=int,
default=0,
help="By default, data recording is resumed. When set to 1, delete the local directory and start data recording from scratch.",
)
parser_record.add_argument(
"-p",
"--pretrained-policy-name-or-path",
type=str,
help=(
"Either the repo ID of a model hosted on the Hub or a path to a directory containing weights "
"saved using `Policy.save_pretrained`."
),
)
parser_record.add_argument(
"--policy-overrides",
type=str,
nargs="*",
help="Any key=value arguments to override config values (use dots for.nested=overrides)",
)
parser_replay = subparsers.add_parser("replay", parents=[base_parser])
parser_replay.add_argument(
"--fps", type=none_or_int, default=None, help="Frames per second (set to None to disable)"
)
parser_replay.add_argument(
"--root",
type=Path,
default="data",
help="Root directory where the dataset will be stored locally at '{root}/{repo_id}' (e.g. 'data/hf_username/dataset_name').",
)
parser_replay.add_argument(
"--repo-id",
type=str,
default="lerobot/test",
help="Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).",
)
parser_replay.add_argument("--episode", type=int, default=0, help="Index of the episode to replay.")
args = parser.parse_args()
init_logging()
control_mode = args.mode
robot_path = args.robot_path
robot_overrides = args.robot_overrides
kwargs = vars(args)
del kwargs["mode"]
del kwargs["robot_path"]
del kwargs["robot_overrides"]
robot_cfg = init_hydra_config(robot_path, robot_overrides)
robot = make_robot(robot_cfg)
if control_mode == "calibrate":
calibrate(robot, **kwargs)
elif control_mode == "teleoperate":
teleoperate(robot, **kwargs)
elif control_mode == "evaluate":
evaluate(robot, **kwargs)
elif control_mode == "llm_agent":
llm_agent(robot, **kwargs)
elif control_mode == "record":
record(robot, **kwargs)
elif control_mode == "replay":
replay(robot, **kwargs)
if robot.is_connected:
# Disconnect manually to avoid a "Core dump" during process
# termination due to camera threads not properly exiting.
robot.disconnect()
================================================
FILE: core/test.py
================================================