lerobot/docs/source/envhub_leisaac.mdx

# LeIsaac × LeRobot EnvHub

LeRobot EnvHub now supports **imitation learning in simulation** with LeIsaac.
Spin up everyday manipulation tasks, teleoperate the robot, collect demos, push them to the Hub, and train policies in LeRobot — all in one loop.

[LeIsaac](https://github.com/LightwheelAI/leisaac) integrates with IsaacLab and the SO101 Leader/Follower setup to provide:

- 🕹️ **Teleoperation-first workflows** for data collection
- 📦 **Built-in data conversion** ready for LeRobot training
- 🤖 **Everyday skills** like picking oranges, lifting cubes, cleaning tables, and folding cloth
- ☁️ **Ongoing upgrades** from [LightWheel](https://lightwheel.ai/): cloud simulation, EnvHub support, Sim2Real tooling, and more

Below you’ll find the currently supported LeIsaac tasks exposed through LeRobot EnvHub.

# Available Environments

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:

```bash
python scripts/environments/list_envs.py
```

| Task                                                                                                                                                            | Environment ID                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | Task Description                                                                                                           | Related Robot                                              |
| :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------- |
| <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                                  |
| <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                                  |
| <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 |
| <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                                      |

# Load LeIsaac directly in LeRobot with one line of code

> EnvHub: Share LeIsaac environments through HuggingFace

[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.

LeIsaac offers EnvHub support so you can consume or share tasks with only a few commands.

<video
  controls
  src="https://github.com/user-attachments/assets/687666f5-ebe0-421d-84a0-eb86116ac5f8"
  style={{ width: "100%", maxWidth: "960px", borderRadius: "8px" }}
/>

## How to get started, environment Setup

Run the following commands to setup your code environments:

```bash
# Refer to Getting Started/Installation to install leisaac firstly
conda create -n leisaac_envhub python=3.11
conda activate leisaac_envhub

conda install -c "nvidia/label/cuda-12.8.1" cuda-toolkit
pip install -U torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu128
pip install 'leisaac[isaaclab] @ git+https://github.com/LightwheelAI/leisaac.git#subdirectory=source/leisaac' --extra-index-url https://pypi.nvidia.com

# Install lerobot
pip install lerobot==0.4.1

# Fix numpy version
pip install numpy==1.26.0
```

## Usage Example

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.

### Random Action

<details>
<summary>Click to expand code example</summary>

```python
# envhub_random_action.py

import torch
from lerobot.envs.factory import make_env

# Load from the hub
envs_dict = make_env("LightwheelAI/leisaac_env:envs/so101_pick_orange.py", n_envs=1, trust_remote_code=True)

# Access the environment
suite_name = next(iter(envs_dict))
sync_vector_env = envs_dict[suite_name][0]
# retrieve the isaac environment from the sync vector env
env = sync_vector_env.envs[0].unwrapped

# Use it like any gym environment
obs, info = env.reset()

while True:
    action = torch.tensor(env.action_space.sample())
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        obs, info = env.reset()

env.close()
```

</details>

```bash
python envhub_random_action.py
```

You should see the SO101 arm swinging under purely random commands.

### Teleoperation

LeRobot’s teleoperation stack can drive the simulated arm.

Connect the SO101 Leader controller, run the calibration command below.

```bash
lerobot-calibrate \
    --teleop.type=so101_leader \
    --teleop.port=/dev/ttyACM0 \
    --teleop.id=leader
```

And then launch the teleop script.

<details>
<summary>Click to expand code example</summary>

```python
# envhub_teleop_example.py

import logging
import time
import gymnasium as gym

from dataclasses import asdict, dataclass
from pprint import pformat

from lerobot.teleoperators import (  # noqa: F401
    Teleoperator,
    TeleoperatorConfig,
    make_teleoperator_from_config,
    so_leader,
    bi_so_leader,
)
from lerobot.utils.robot_utils import precise_sleep
from lerobot.utils.utils import init_logging
from lerobot.envs.factory import make_env


@dataclass
class TeleoperateConfig:
    teleop: TeleoperatorConfig
    env_name: str = "so101_pick_orange"
    fps: int = 60


@dataclass
class EnvWrap:
    env: gym.Env


def make_env_from_leisaac(env_name: str = "so101_pick_orange"):
    envs_dict = make_env(
        f'LightwheelAI/leisaac_env:envs/{env_name}.py',
        n_envs=1,
        trust_remote_code=True
    )
    suite_name = next(iter(envs_dict))
    sync_vector_env = envs_dict[suite_name][0]
    env = sync_vector_env.envs[0].unwrapped

    return env


def teleop_loop(teleop: Teleoperator, env: gym.Env, fps: int):
    from leisaac.devices.action_process import preprocess_device_action
    from leisaac.assets.robots.lerobot import SO101_FOLLOWER_MOTOR_LIMITS
    from leisaac.utils.env_utils import dynamic_reset_gripper_effort_limit_sim

    env_wrap = EnvWrap(env=env)

    obs, info = env.reset()
    while True:
        loop_start = time.perf_counter()
        if env.cfg.dynamic_reset_gripper_effort_limit:
            dynamic_reset_gripper_effort_limit_sim(env, 'so101leader')

        raw_action = teleop.get_action()
        processed_action = preprocess_device_action(
            dict(
                so101_leader=True,
                joint_state={
                    k.removesuffix(".pos"): v for k, v in raw_action.items()},
                motor_limits=SO101_FOLLOWER_MOTOR_LIMITS),
            env_wrap
        )
        obs, reward, terminated, truncated, info = env.step(processed_action)
        if terminated or truncated:
            obs, info = env.reset()

        dt_s = time.perf_counter() - loop_start
        precise_sleep(max(1 / fps - dt_s, 0.0))
        loop_s = time.perf_counter() - loop_start
        print(f"\ntime: {loop_s * 1e3:.2f}ms ({1 / loop_s:.0f} Hz)")


def teleoperate(cfg: TeleoperateConfig):
    init_logging()
    logging.info(pformat(asdict(cfg)))

    teleop = make_teleoperator_from_config(cfg.teleop)
    env = make_env_from_leisaac(cfg.env_name)

    teleop.connect()
    if hasattr(env, 'initialize'):
        env.initialize()
    try:
        teleop_loop(teleop=teleop, env=env, fps=cfg.fps)
    except KeyboardInterrupt:
        pass
    finally:
        teleop.disconnect()
        env.close()


def main():
    teleoperate(TeleoperateConfig(
        teleop=so_leader.SO101LeaderConfig(
            port="/dev/ttyACM0",
            id='leader',
            use_degrees=False,
        ),
        env_name="so101_pick_orange",
        fps=60,
    ))


if __name__ == "__main__":
    main()

```

</details>

```bash
python envhub_teleop_example.py
```

Running the script lets you operate the simulated arm using the physical Leader device.

## ☁️ Cloud Simulation (No GPU Required)

Don’t have a local GPU or the right drivers? No problem! You can run LeIsaac entirely in the cloud with zero setup.
LeIsaac works out-of-the-box on **NVIDIA Brev**, giving you a fully configured environment directly in your browser.

👉 **Start here:** [https://lightwheelai.github.io/leisaac/docs/cloud_simulation/nvidia_brev](https://lightwheelai.github.io/leisaac/docs/cloud_simulation/nvidia_brev)

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.

**No GPU, no drivers, no local installation. Just click and run.**

## Additional Notes

We keep EnvHub coverage aligned with the LeIsaac task. Currently supported:

- `so101_pick_orange`
- `so101_lift_cube`
- `so101_clean_toytable`
- `bi_so101_fold_cloth`

Switch tasks by targeting a different script when calling `make_env`, for example:

```python
envs_dict_pick_orange = make_env("LightwheelAI/leisaac_env:envs/so101_pick_orange.py", n_envs=1, trust_remote_code=True)
envs_dict_lift_cube = make_env("LightwheelAI/leisaac_env:envs/so101_lift_cube.py", n_envs=1, trust_remote_code=True)
envs_dict_clean_toytable = make_env("LightwheelAI/leisaac_env:envs/so101_clean_toytable.py", n_envs=1, trust_remote_code=True)
envs_dict_fold_cloth = make_env("LightwheelAI/leisaac_env:envs/bi_so101_fold_cloth.py", n_envs=1, trust_remote_code=True)
```

Note: when working with `bi_so101_fold_cloth`, call `initialize()` immediately after retrieving the env before performing any other operations:

<details>
<summary>Click to expand code example</summary>

```python
import torch
from lerobot.envs.factory import make_env

# Load from the hub
envs_dict = make_env("LightwheelAI/leisaac_env:envs/bi_so101_fold_cloth.py", n_envs=1, trust_remote_code=True)

# Access the environment
suite_name = next(iter(envs_dict))
sync_vector_env = envs_dict[suite_name][0]
# retrieve the isaac environment from the sync vector env
env = sync_vector_env.envs[0].unwrapped

# NOTE: initialize() first
env.initialize()

# other operation with env...
```

</details>