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0d80452 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 | # 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 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 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 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>
|