lerobot/docs/source/envhub.mdx

# Loading Environments from the Hub

The **EnvHub** feature allows you to load simulation environments directly from the Hugging Face Hub with a single line of code. This unlocks a powerful new model for collaboration: instead of environments being locked away inside monolithic libraries, anyone can publish custom environments and share them with the community.

## Overview

With EnvHub, you can:

- Load environments from the Hub instantly
- Share your custom simulation tasks with the community
- Version control your environments using Git
- Distribute complex physics simulations without packaging hassles

## Quick Start

Loading an environment from the Hub is as simple as:

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

# Load a hub environment (requires explicit consent to run remote code)
env = make_env("lerobot/cartpole-env", trust_remote_code=True)
```

<Tip warning={true}>
  **Security Notice**: Loading environments from the Hub executes Python code
  from third-party repositories. Only use `trust_remote_code=True` with
  repositories you trust. We strongly recommend pinning to a specific commit
  hash for reproducibility and security.
</Tip>

## What is EnvHub?

EnvHub is a framework that allows researchers and developers to:

1. **Publish environments** to the Hugging Face Hub as Git repositories
2. **Load environments** dynamically without installing them as packages
3. **Version and track** environment changes using Git semantics
4. **Discover** new simulation tasks shared by the community

This design means you can go from discovering an interesting environment on the Hub to running experiments in seconds, without worrying about dependency conflicts or complex installation procedures.

## Repository Structure

To make your environment loadable from the Hub, your repository must contain at minimum:

### Required Files

**`env.py`** (or custom Python file)

- Must expose a `make_env(n_envs: int, use_async_envs: bool)` function
- This function should return one of:
  - A `gym.vector.VectorEnv` (most common)
  - A single `gym.Env` (will be automatically wrapped)
  - A dict mapping `{suite_name: {task_id: VectorEnv}}` (for multi-task benchmarks)

### Optional Files

**`requirements.txt`**

- List any additional dependencies your environment needs
- Users will need to install these manually before loading your environment

**`README.md`**

- Document your environment: what task it implements, observation/action spaces, rewards, etc.
- Include usage examples and any special setup instructions

**`.gitignore`**

- Exclude unnecessary files from your repository

### Example Repository Structure

```
my-environment-repo/
├── env.py                 # Main environment definition (required)
├── requirements.txt       # Dependencies (optional)
├── README.md             # Documentation (recommended)
├── assets/               # Images, videos, etc. (optional)
│   └── demo.gif
└── configs/              # Config files if needed (optional)
    └── task_config.yaml
```

## Creating Your Environment Repository

### Step 1: Define Your Environment

Create an `env.py` file with a `make_env` function:

```python
# env.py
import gymnasium as gym

def make_env(n_envs: int = 1, use_async_envs: bool = False):
    """
    Create vectorized environments for your custom task.

    Args:
        n_envs: Number of parallel environments
        use_async_envs: Whether to use AsyncVectorEnv or SyncVectorEnv

    Returns:
        gym.vector.VectorEnv or dict mapping suite names to vectorized envs
    """
    def _make_single_env():
        # Create your custom environment
        return gym.make("CartPole-v1")

    # Choose vector environment type
    env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv

    # Create vectorized environment
    vec_env = env_cls([_make_single_env for _ in range(n_envs)])

    return vec_env
```

### Step 2: Test Locally

Before uploading, test your environment locally:

```python
from lerobot.envs.utils import _load_module_from_path, _call_make_env, _normalize_hub_result

# Load your module
module = _load_module_from_path("./env.py")

# Test the make_env function
result = _call_make_env(module, n_envs=2, use_async_envs=False)
normalized = _normalize_hub_result(result)

# Verify it works
suite_name = next(iter(normalized))
env = normalized[suite_name][0]
obs, info = env.reset()
print(f"Observation shape: {obs.shape if hasattr(obs, 'shape') else type(obs)}")
env.close()
```

### Step 3: Upload to the Hub

Upload your repository to Hugging Face:

```bash
# Install huggingface_hub if needed
pip install huggingface_hub

# Login to Hugging Face
huggingface-cli login

# Create a new repository
huggingface-cli repo create my-custom-env --type space --org my-org

# Initialize git and push
git init
git add .
git commit -m "Initial environment implementation"
git remote add origin https://huggingface.co/my-org/my-custom-env
git push -u origin main
```

Alternatively, use the `huggingface_hub` Python API:

```python
from huggingface_hub import HfApi

api = HfApi()

# Create repository
api.create_repo("my-custom-env", repo_type="space")

# Upload files
api.upload_folder(
    folder_path="./my-env-folder",
    repo_id="username/my-custom-env",
    repo_type="space",
)
```

## Loading Environments from the Hub

### Basic Usage

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

# Load from the hub
envs_dict = make_env(
    "username/my-custom-env",
    n_envs=4,
    trust_remote_code=True
)

# Access the environment
suite_name = next(iter(envs_dict))
env = envs_dict[suite_name][0]

# Use it like any gym environment
obs, info = env.reset()
action = env.action_space.sample()
obs, reward, terminated, truncated, info = env.step(action)
```

### Advanced: Pinning to Specific Versions

For reproducibility and security, pin to a specific Git revision:

```python
# Pin to a specific branch
env = make_env("username/my-env@main", trust_remote_code=True)

# Pin to a specific commit (recommended for papers/experiments)
env = make_env("username/my-env@abc123def456", trust_remote_code=True)

# Pin to a tag
env = make_env("username/my-env@v1.0.0", trust_remote_code=True)
```

### Custom File Paths

If your environment definition is not in `env.py`:

```python
# Load from a custom file
env = make_env("username/my-env:custom_env.py", trust_remote_code=True)

# Combine with version pinning
env = make_env("username/my-env@v1.0:envs/task_a.py", trust_remote_code=True)
```

### Async Environments

For better performance with multiple environments:

```python
envs_dict = make_env(
    "username/my-env",
    n_envs=8,
    use_async_envs=True,  # Use AsyncVectorEnv for parallel execution
    trust_remote_code=True
)
```

## URL Format Reference

The hub URL format supports several patterns:

| Pattern              | Description                    | Example                                |
| -------------------- | ------------------------------ | -------------------------------------- |
| `user/repo`          | Load `env.py` from main branch | `make_env("lerobot/pusht-env")`        |
| `user/repo@revision` | Load from specific revision    | `make_env("lerobot/pusht-env@main")`   |
| `user/repo:path`     | Load custom file               | `make_env("lerobot/envs:pusht.py")`    |
| `user/repo@rev:path` | Revision + custom file         | `make_env("lerobot/envs@v1:pusht.py")` |

## Multi-Task Environments

For benchmarks with multiple tasks (like LIBERO), return a nested dictionary:

```python
def make_env(n_envs: int = 1, use_async_envs: bool = False):
    env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv

    # Return dict: {suite_name: {task_id: VectorEnv}}
    return {
        "suite_1": {
            0: env_cls([lambda: gym.make("Task1-v0") for _ in range(n_envs)]),
            1: env_cls([lambda: gym.make("Task2-v0") for _ in range(n_envs)]),
        },
        "suite_2": {
            0: env_cls([lambda: gym.make("Task3-v0") for _ in range(n_envs)]),
        }
    }
```

## Security Considerations

<Tip warning={true}>
  **Important**: The `trust_remote_code=True` flag is required to execute
  environment code from the Hub. This is by design for security.
</Tip>

When loading environments from the Hub:

1. **Review the code first**: Visit the repository and inspect `env.py` before loading
2. **Pin to commits**: Use specific commit hashes for reproducibility
3. **Check dependencies**: Review `requirements.txt` for suspicious packages
4. **Use trusted sources**: Prefer official organizations or well-known researchers
5. **Sandbox if needed**: Run untrusted code in isolated environments (containers, VMs)

Example of safe usage:

```python
# ❌ BAD: Loading without inspection
env = make_env("random-user/untrusted-env", trust_remote_code=True)

# ✅ GOOD: Review code, then pin to specific commit
# 1. Visit https://huggingface.co/trusted-org/verified-env
# 2. Review the env.py file
# 3. Copy the commit hash
env = make_env("trusted-org/verified-env@a1b2c3d4", trust_remote_code=True)
```

## Example: CartPole from the Hub

Here's a complete example using the reference CartPole environment:

```python
from lerobot.envs.factory import make_env
import numpy as np

# Load the environment
envs_dict = make_env("lerobot/cartpole-env", n_envs=4, trust_remote_code=True)

# Get the vectorized environment
suite_name = next(iter(envs_dict))
env = envs_dict[suite_name][0]

# Run a simple episode
obs, info = env.reset()
done = np.zeros(env.num_envs, dtype=bool)
total_reward = np.zeros(env.num_envs)

while not done.all():
    # Random policy
    action = env.action_space.sample()
    obs, reward, terminated, truncated, info = env.step(action)
    total_reward += reward
    done = terminated | truncated

print(f"Average reward: {total_reward.mean():.2f}")
env.close()
```

## Benefits of EnvHub

### For Environment Authors

- **Easy distribution**: No PyPI packaging required
- **Version control**: Use Git for environment versioning
- **Rapid iteration**: Push updates instantly
- **Documentation**: Hub README renders beautifully
- **Community**: Reach LeRobot users directly

### For Researchers

- **Quick experiments**: Load any environment in one line
- **Reproducibility**: Pin to specific commits
- **Discovery**: Browse environments on the Hub
- **No conflicts**: No need to install conflicting packages

### For the Community

- **Growing ecosystem**: More diverse simulation tasks
- **Standardization**: Common `make_env` API
- **Collaboration**: Fork and improve existing environments
- **Accessibility**: Lower barrier to sharing research

## Troubleshooting

### "Refusing to execute remote code"

You must explicitly pass `trust_remote_code=True`:

```python
env = make_env("user/repo", trust_remote_code=True)
```

### "Module X not found"

The hub environment has dependencies you need to install:

```bash
# Check the repo's requirements.txt and install dependencies
pip install gymnasium numpy
```

### "make_env not found in module"

Your `env.py` must expose a `make_env` function:

```python
def make_env(n_envs: int, use_async_envs: bool):
    # Your implementation
    pass
```

### Environment returns wrong type

The `make_env` function must return:

- A `gym.vector.VectorEnv`, or
- A single `gym.Env`, or
- A dict `{suite_name: {task_id: VectorEnv}}`

## Best Practices

1. **Document your environment**: Include observation/action space descriptions, reward structure, and termination conditions in your README
2. **Add requirements.txt**: List all dependencies with versions
3. **Test thoroughly**: Verify your environment works locally before pushing
4. **Use semantic versioning**: Tag releases with version numbers
5. **Add examples**: Include usage examples in your README
6. **Keep it simple**: Minimize dependencies when possible
7. **License your work**: Add a LICENSE file to clarify usage terms

## Future Directions

The EnvHub ecosystem enables exciting possibilities:

- **GPU-accelerated physics**: Share Isaac Gym or Brax environments
- **Photorealistic rendering**: Distribute environments with advanced graphics
- **Multi-agent scenarios**: Complex interaction tasks
- **Real-world simulators**: Digital twins of physical setups
- **Procedural generation**: Infinite task variations
- **Domain randomization**: Pre-configured DR pipelines

As more researchers and developers contribute, the diversity and quality of available environments will grow, benefiting the entire robotics learning community.

## See Also

- [Hugging Face Hub Documentation](https://huggingface.co/docs/hub/en/index)
- [Gymnasium Documentation](https://gymnasium.farama.org/index.html)
- [Example Hub Environment](https://huggingface.co/lerobot/cartpole-env)