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docs: Add LeIsaac x LeRobot Envhub tutorial (#2498)
* add leisaac doc * depreciate il in sim * fix readme * more * fix styling * update title * more changes * more * fix style * more * fix style
This commit is contained in:
Jade Choghari
@@ -47,8 +47,8 @@
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- sections:
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- local: envhub
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title: Environments from the Hub
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- local: il_sim
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title: Imitation Learning in Sim
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- local: envhub_leisaac
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title: Control & Train Robots in Sim (LeIsaac)
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- local: libero
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title: Using Libero
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- local: metaworld
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@@ -0,0 +1,301 @@
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# 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 busy_wait
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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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busy_wait(1 / fps - dt_s)
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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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@@ -1,220 +0,0 @@
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# Imitation Learning in Sim
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This tutorial will explain how to train a neural network to control a robot in simulation with imitation learning.
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**You'll learn:**
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1. How to record a dataset in simulation with [gym-hil](https://github.com/huggingface/gym-hil) and visualize the dataset.
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2. How to train a policy using your data.
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3. How to evaluate your policy in simulation and visualize the results.
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For the simulation environment we use the same [repo](https://github.com/huggingface/gym-hil) that is also being used by the Human-In-the-Loop (HIL) reinforcement learning algorithm.
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This environment is based on [MuJoCo](https://mujoco.org) and allows you to record datasets in LeRobotDataset format.
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Teleoperation is easiest with a controller like the Logitech F710, but you can also use your keyboard if you are up for the challenge.
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## Installation
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First, install the `gym_hil` package within the LeRobot environment, go to your LeRobot folder and run this command:
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```bash
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pip install -e ".[hilserl]"
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```
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## Teleoperate and Record a Dataset
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To use `gym_hil` with LeRobot, you need to use a configuration file. An example config file can be found [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/sim_il/env_config.json).
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To teleoperate and collect a dataset, we need to modify this config file. Here's an example configuration for imitation learning data collection:
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```json
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{
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"env": {
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"type": "gym_manipulator",
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"name": "gym_hil",
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"task": "PandaPickCubeGamepad-v0",
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"fps": 10
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},
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"dataset": {
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"repo_id": "your_username/il_gym",
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"root": null,
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"task": "pick_cube",
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"num_episodes_to_record": 30,
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"replay_episode": null,
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"push_to_hub": true
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},
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"mode": "record",
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"device": "cuda"
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}
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```
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Key configuration points:
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- Set your `repo_id` in the `dataset` section: `"repo_id": "your_username/il_gym"`
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- Set `num_episodes_to_record: 30` to collect 30 demonstration episodes
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- Ensure `mode` is set to `"record"`
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- If you don't have an NVIDIA GPU, change `"device": "cuda"` to `"mps"` for macOS or `"cpu"`
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- To use keyboard instead of gamepad, change `"task"` to `"PandaPickCubeKeyboard-v0"`
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Then we can run this command to start:
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<hfoptions id="teleop_sim">
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<hfoption id="Linux">
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```bash
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python -m lerobot.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
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```
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</hfoption>
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<hfoption id="MacOS">
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```bash
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mjpython -m lerobot.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
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```
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</hfoption>
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</hfoptions>
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Once rendered you can teleoperate the robot with the gamepad or keyboard, below you can find the gamepad/keyboard controls.
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Note that to teleoperate the robot you have to hold the "Human Take Over Pause Policy" Button `RB` to enable control!
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**Gamepad Controls**
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<p align="center">
|
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<img
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src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/gamepad_guide.jpg?raw=true"
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||||
alt="Figure shows the control mappings on a Logitech gamepad."
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title="Gamepad Control Mapping"
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width="100%"
|
||||
></img>
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</p>
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<p align="center">
|
||||
<i>Gamepad button mapping for robot control and episode management</i>
|
||||
</p>
|
||||
|
||||
**Keyboard controls**
|
||||
|
||||
For keyboard controls use the `spacebar` to enable control and the following keys to move the robot:
|
||||
|
||||
```bash
|
||||
Arrow keys: Move in X-Y plane
|
||||
Shift and Shift_R: Move in Z axis
|
||||
Right Ctrl and Left Ctrl: Open and close gripper
|
||||
ESC: Exit
|
||||
```
|
||||
|
||||
## Visualize a dataset
|
||||
|
||||
If you uploaded your dataset to the hub you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id.
|
||||
|
||||
<p align="center">
|
||||
<img
|
||||
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/dataset_visualizer_sim.png"
|
||||
alt="Figure shows the dataset visualizer"
|
||||
title="Dataset visualization"
|
||||
width="100%"
|
||||
></img>
|
||||
</p>
|
||||
<p align="center">
|
||||
<i>Dataset visualizer</i>
|
||||
</p>
|
||||
|
||||
## Train a policy
|
||||
|
||||
To train a policy to control your robot, use the [`lerobot-train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
|
||||
|
||||
```bash
|
||||
lerobot-train \
|
||||
--dataset.repo_id=${HF_USER}/il_gym \
|
||||
--policy.type=act \
|
||||
--output_dir=outputs/train/il_sim_test \
|
||||
--job_name=il_sim_test \
|
||||
--policy.device=cuda \
|
||||
--wandb.enable=true
|
||||
```
|
||||
|
||||
Let's explain the command:
|
||||
|
||||
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/il_gym`.
|
||||
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
|
||||
3. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
|
||||
4. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
|
||||
|
||||
Training should take several hours, 100k steps (which is the default) will take about 1h on Nvidia A100. You will find checkpoints in `outputs/train/il_sim_test/checkpoints`.
|
||||
|
||||
#### Train using Collab
|
||||
|
||||
If your local computer doesn't have a powerful GPU you could utilize Google Collab to train your model by following the [ACT training notebook](./notebooks#training-act).
|
||||
|
||||
#### Upload policy checkpoints
|
||||
|
||||
Once training is done, upload the latest checkpoint with:
|
||||
|
||||
```bash
|
||||
huggingface-cli upload ${HF_USER}/il_sim_test \
|
||||
outputs/train/il_sim_test/checkpoints/last/pretrained_model
|
||||
```
|
||||
|
||||
You can also upload intermediate checkpoints with:
|
||||
|
||||
```bash
|
||||
CKPT=010000
|
||||
huggingface-cli upload ${HF_USER}/il_sim_test${CKPT} \
|
||||
outputs/train/il_sim_test/checkpoints/${CKPT}/pretrained_model
|
||||
```
|
||||
|
||||
## Evaluate your policy in Sim
|
||||
|
||||
To evaluate your policy we have to use a configuration file. An example can be found [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/sim_il/eval_config.json).
|
||||
|
||||
Here's an example evaluation configuration:
|
||||
|
||||
```json
|
||||
{
|
||||
"env": {
|
||||
"type": "gym_manipulator",
|
||||
"name": "gym_hil",
|
||||
"task": "PandaPickCubeGamepad-v0",
|
||||
"fps": 10
|
||||
},
|
||||
"dataset": {
|
||||
"repo_id": "your_username/il_sim_dataset",
|
||||
"dataset_root": null,
|
||||
"task": "pick_cube"
|
||||
},
|
||||
"pretrained_policy_name_or_path": "your_username/il_sim_model",
|
||||
"device": "cuda"
|
||||
}
|
||||
```
|
||||
|
||||
Make sure to replace:
|
||||
|
||||
- `repo_id` with the dataset you trained on (e.g., `your_username/il_sim_dataset`)
|
||||
- `pretrained_policy_name_or_path` with your model ID (e.g., `your_username/il_sim_model`)
|
||||
|
||||
Then you can run this command to visualize your trained policy
|
||||
|
||||
<hfoptions id="eval_policy">
|
||||
<hfoption id="Linux">
|
||||
|
||||
```bash
|
||||
python -m lerobot.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
|
||||
```
|
||||
|
||||
</hfoption>
|
||||
<hfoption id="MacOS">
|
||||
|
||||
```bash
|
||||
mjpython -m lerobot.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
|
||||
```
|
||||
|
||||
</hfoption>
|
||||
</hfoptions>
|
||||
|
||||
> [!WARNING]
|
||||
> While the main workflow of training ACT in simulation is straightforward, there is significant room for exploring how to set up the task, define the initial state of the environment, and determine the type of data required during collection to learn the most effective policy. If your trained policy doesn't perform well, investigate the quality of the dataset it was trained on using our visualizers, as well as the action values and various hyperparameters related to ACT and the simulation.
|
||||
|
||||
Congrats 🎉, you have finished this tutorial. If you want to continue with using LeRobot in simulation follow this [Tutorial on reinforcement learning in sim with HIL-SERL](https://huggingface.co/docs/lerobot/hilserl_sim)
|
||||
|
||||
> [!TIP]
|
||||
> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
|
||||
Reference in New Issue
Block a user