mirror of
https://github.com/huggingface/lerobot.git
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Steven Palma
302 lines
14 KiB
Plaintext
302 lines
14 KiB
Plaintext
# LeIsaac × LeRobot EnvHub
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LeRobot EnvHub now supports **imitation learning in simulation** with LeIsaac.
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Spin up everyday manipulation tasks, teleoperate the robot, collect demos, push them to the Hub, and train policies in LeRobot — all in one loop.
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[LeIsaac](https://github.com/LightwheelAI/leisaac) integrates with IsaacLab and the SO101 Leader/Follower setup to provide:
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- 🕹️ **Teleoperation-first workflows** for data collection
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- 📦 **Built-in data conversion** ready for LeRobot training
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- 🤖 **Everyday skills** like picking oranges, lifting cubes, cleaning tables, and folding cloth
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- ☁️ **Ongoing upgrades** from [LightWheel](https://lightwheel.ai/): cloud simulation, EnvHub support, Sim2Real tooling, and more
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Below you’ll find the currently supported LeIsaac tasks exposed through LeRobot EnvHub.
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# Available Environments
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The following table lists all available tasks and environments in LeIsaac x LeRobot Envhub. You can also get the latest list of environments by running the following command:
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```bash
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python scripts/environments/list_envs.py
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```
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| Task | Environment ID | Task Description | Related Robot |
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| :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------- |
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| <video src="https://github.com/user-attachments/assets/466eddff-f720-4f99-94d5-5e123e4c302c" autoplay loop muted playsinline style="max-width: 300px;"></video> | [LeIsaac-SO101-PickOrange-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/pick_orange/pick_orange_env_cfg.py)<br /><br />[LeIsaac-SO101-PickOrange-Direct-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/pick_orange/direct/pick_orange_env.py) | Pick three oranges and put them into the plate, then reset the arm to rest state. | Single-Arm SO101 Follower |
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| <video src="https://github.com/user-attachments/assets/1e4eb83a-0b38-40fb-a0b2-ddb0fe201e6d" autoplay loop muted playsinline style="max-width: 300px;"></video> | [LeIsaac-SO101-LiftCube-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/lift_cube/lift_cube_env_cfg.py)<br /><br />[LeIsaac-SO101-LiftCube-Direct-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/lift_cube/direct/lift_cube_env.py) | Lift the red cube up. | Single-Arm SO101 Follower |
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| <video src="https://github.com/user-attachments/assets/e49d8f1c-dcc9-412b-a88f-100680d8a45b" autoplay loop muted playsinline style="max-width: 300px;"></video> | [LeIsaac-SO101-CleanToyTable-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/clean_toy_table/clean_toy_table_env_cfg.py)<br /><br />[LeIsaac-SO101-CleanToyTable-BiArm-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/clean_toy_table/clean_toy_table_bi_arm_env_cfg.py)<br /><br />[LeIsaac-SO101-CleanToyTable-BiArm-Direct-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/clean_toy_table/direct/clean_toy_table_bi_arm_env.py) | Pick two letter e objects into the box, and reset the arm to rest state. | Single-Arm SO101 Follower<br /><br />Bi-Arm SO101 Follower |
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| <video src="https://github.com/user-attachments/assets/e29a0f8a-9286-4ce6-b45d-342c3d3ba754" autoplay loop muted playsinline style="max-width: 300px;"></video> | [LeIsaac-SO101-FoldCloth-BiArm-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/fold_cloth/fold_cloth_bi_arm_env_cfg.py)<br /><br />[LeIsaac-SO101-FoldCloth-BiArm-Direct-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/fold_cloth/direct/fold_cloth_bi_arm_env.py) | Fold the cloth, and reset the arm to rest state.<br /><br />_Note: Only the DirectEnv support check_success in this task._ | Bi-Arm SO101 Follower |
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# Load LeIsaac directly in LeRobot with one line of code
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> EnvHub: Share LeIsaac environments through HuggingFace
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[EnvHub](https://huggingface.co/docs/lerobot/envhub) is our reproducible environment hub, spin up a packaged simulation with one line, experiment immediately, and publish your own tasks for the community.
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LeIsaac offers EnvHub support so you can consume or share tasks with only a few commands.
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<video
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controls
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src="https://github.com/user-attachments/assets/687666f5-ebe0-421d-84a0-eb86116ac5f8"
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style={{ width: "100%", maxWidth: "960px", borderRadius: "8px" }}
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/>
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## How to get started, environment Setup
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Run the following commands to setup your code environments:
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```bash
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# Refer to Getting Started/Installation to install leisaac firstly
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conda create -n leisaac_envhub python=3.11
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conda activate leisaac_envhub
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conda install -c "nvidia/label/cuda-12.8.1" cuda-toolkit
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pip install -U torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu128
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pip install 'leisaac[isaaclab] @ git+https://github.com/LightwheelAI/leisaac.git#subdirectory=source/leisaac' --extra-index-url https://pypi.nvidia.com
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# Install lerobot
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pip install lerobot==0.4.1
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# Fix numpy version
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pip install numpy==1.26.0
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```
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## Usage Example
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EnvHub exposes every LeIsaac-supported task in a uniform interface. The examples below load `so101_pick_orange` and demonstrate a random-action rollout and an interactive teleoperation.
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### Random Action
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<details>
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<summary>Click to expand code example</summary>
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```python
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# envhub_random_action.py
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import torch
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from lerobot.envs.factory import make_env
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# Load from the hub
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envs_dict = make_env("LightwheelAI/leisaac_env:envs/so101_pick_orange.py", n_envs=1, trust_remote_code=True)
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# Access the environment
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suite_name = next(iter(envs_dict))
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sync_vector_env = envs_dict[suite_name][0]
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# retrieve the isaac environment from the sync vector env
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env = sync_vector_env.envs[0].unwrapped
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# Use it like any gym environment
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obs, info = env.reset()
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while True:
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action = torch.tensor(env.action_space.sample())
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obs, reward, terminated, truncated, info = env.step(action)
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if terminated or truncated:
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obs, info = env.reset()
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env.close()
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```
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</details>
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```bash
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python envhub_random_action.py
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```
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You should see the SO101 arm swinging under purely random commands.
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### Teleoperation
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LeRobot’s teleoperation stack can drive the simulated arm.
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Connect the SO101 Leader controller, run the calibration command below.
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```bash
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lerobot-calibrate \
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--teleop.type=so101_leader \
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--teleop.port=/dev/ttyACM0 \
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--teleop.id=leader
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```
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And then launch the teleop script.
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<details>
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<summary>Click to expand code example</summary>
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```python
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# envhub_teleop_example.py
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import logging
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import time
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import gymnasium as gym
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from dataclasses import asdict, dataclass
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from pprint import pformat
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from lerobot.teleoperators import ( # noqa: F401
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Teleoperator,
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TeleoperatorConfig,
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make_teleoperator_from_config,
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so101_leader,
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)
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from lerobot.utils.robot_utils import precise_sleep
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from lerobot.utils.utils import init_logging
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from lerobot.envs.factory import make_env
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@dataclass
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class TeleoperateConfig:
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teleop: TeleoperatorConfig
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env_name: str = "so101_pick_orange"
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fps: int = 60
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@dataclass
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class EnvWrap:
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env: gym.Env
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def make_env_from_leisaac(env_name: str = "so101_pick_orange"):
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envs_dict = make_env(
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f'LightwheelAI/leisaac_env:envs/{env_name}.py',
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n_envs=1,
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trust_remote_code=True
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)
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suite_name = next(iter(envs_dict))
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sync_vector_env = envs_dict[suite_name][0]
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env = sync_vector_env.envs[0].unwrapped
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return env
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def teleop_loop(teleop: Teleoperator, env: gym.Env, fps: int):
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from leisaac.devices.action_process import preprocess_device_action
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from leisaac.assets.robots.lerobot import SO101_FOLLOWER_MOTOR_LIMITS
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from leisaac.utils.env_utils import dynamic_reset_gripper_effort_limit_sim
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env_wrap = EnvWrap(env=env)
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obs, info = env.reset()
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while True:
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loop_start = time.perf_counter()
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if env.cfg.dynamic_reset_gripper_effort_limit:
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dynamic_reset_gripper_effort_limit_sim(env, 'so101leader')
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raw_action = teleop.get_action()
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processed_action = preprocess_device_action(
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dict(
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so101_leader=True,
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joint_state={
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k.removesuffix(".pos"): v for k, v in raw_action.items()},
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motor_limits=SO101_FOLLOWER_MOTOR_LIMITS),
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env_wrap
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)
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obs, reward, terminated, truncated, info = env.step(processed_action)
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if terminated or truncated:
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obs, info = env.reset()
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dt_s = time.perf_counter() - loop_start
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precise_sleep(max(1 / fps - dt_s, 0.0))
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loop_s = time.perf_counter() - loop_start
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print(f"\ntime: {loop_s * 1e3:.2f}ms ({1 / loop_s:.0f} Hz)")
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def teleoperate(cfg: TeleoperateConfig):
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init_logging()
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logging.info(pformat(asdict(cfg)))
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teleop = make_teleoperator_from_config(cfg.teleop)
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env = make_env_from_leisaac(cfg.env_name)
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teleop.connect()
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if hasattr(env, 'initialize'):
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env.initialize()
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try:
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teleop_loop(teleop=teleop, env=env, fps=cfg.fps)
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except KeyboardInterrupt:
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pass
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finally:
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teleop.disconnect()
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env.close()
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def main():
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teleoperate(TeleoperateConfig(
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teleop=so101_leader.SO101LeaderConfig(
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port="/dev/ttyACM0",
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id='leader',
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use_degrees=False,
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),
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env_name="so101_pick_orange",
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fps=60,
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))
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if __name__ == "__main__":
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main()
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```
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</details>
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```bash
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python envhub_teleop_example.py
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```
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Running the script lets you operate the simulated arm using the physical Leader device.
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## ☁️ Cloud Simulation (No GPU Required)
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Don’t have a local GPU or the right drivers? No problem! You can run LeIsaac entirely in the cloud with zero setup.
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LeIsaac works out-of-the-box on **NVIDIA Brev**, giving you a fully configured environment directly in your browser.
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👉 **Start here:** [https://lightwheelai.github.io/leisaac/docs/cloud_simulation/nvidia_brev](https://lightwheelai.github.io/leisaac/docs/cloud_simulation/nvidia_brev)
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Once your instance is deployed, simply open the link for **port 80 (HTTP)** to launch **Visual Studio Code Server** (default password: `password`). From there, you can run simulations, edit code, and visualize IsaacLab environments — all from your web browser.
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**No GPU, no drivers, no local installation. Just click and run.**
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## Additional Notes
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We keep EnvHub coverage aligned with the LeIsaac task. Currently supported:
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- `so101_pick_orange`
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- `so101_lift_cube`
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- `so101_clean_toytable`
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- `bi_so101_fold_cloth`
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Switch tasks by targeting a different script when calling `make_env`, for example:
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```python
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envs_dict_pick_orange = make_env("LightwheelAI/leisaac_env:envs/so101_pick_orange.py", n_envs=1, trust_remote_code=True)
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envs_dict_lift_cube = make_env("LightwheelAI/leisaac_env:envs/so101_lift_cube.py", n_envs=1, trust_remote_code=True)
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envs_dict_clean_toytable = make_env("LightwheelAI/leisaac_env:envs/so101_clean_toytable.py", n_envs=1, trust_remote_code=True)
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envs_dict_fold_cloth = make_env("LightwheelAI/leisaac_env:envs/bi_so101_fold_cloth.py", n_envs=1, trust_remote_code=True)
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```
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Note: when working with `bi_so101_fold_cloth`, call `initialize()` immediately after retrieving the env before performing any other operations:
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<details>
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<summary>Click to expand code example</summary>
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```python
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import torch
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from lerobot.envs.factory import make_env
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# Load from the hub
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envs_dict = make_env("LightwheelAI/leisaac_env:envs/bi_so101_fold_cloth.py", n_envs=1, trust_remote_code=True)
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# Access the environment
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suite_name = next(iter(envs_dict))
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sync_vector_env = envs_dict[suite_name][0]
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# retrieve the isaac environment from the sync vector env
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env = sync_vector_env.envs[0].unwrapped
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# NOTE: initialize() first
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env.initialize()
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# other operation with env...
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```
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</details>
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