# 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 |
| :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------- |
| | [LeIsaac-SO101-PickOrange-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/pick_orange/pick_orange_env_cfg.py)
[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 |
| | [LeIsaac-SO101-LiftCube-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/lift_cube/lift_cube_env_cfg.py)
[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 |
| | [LeIsaac-SO101-CleanToyTable-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/clean_toy_table/clean_toy_table_env_cfg.py)
[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)
[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
Bi-Arm SO101 Follower |
| | [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)
[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.
_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.
## 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
Click to expand code example
```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()
```
```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.
Click to expand code example
```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,
so101_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=so101_leader.SO101LeaderConfig(
port="/dev/ttyACM0",
id='leader',
use_degrees=False,
),
env_name="so101_pick_orange",
fps=60,
))
if __name__ == "__main__":
main()
```
```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:
Click to expand code example
```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...
```