lerobot/docs/source/bring_your_own_policies.mdx

# Bring Your Own Policies

This tutorial explains how to integrate your own custom policy implementations into the LeRobot ecosystem, allowing you to leverage all LeRobot tools for training, evaluation, and deployment while using your own algorithms.

## Step 1: Create a Policy Package

Your custom policy should be organized as an installable Python package following LeRobot's plugin conventions.

### Package Structure

Create a package with the prefix `lerobot_policy_` (IMPORTANT!) followed by your policy name:

```bash
lerobot_policy_my_custom_policy/
├── pyproject.toml
└── src/
    └── lerobot_policy_my_custom_policy/
        ├── __init__.py
        ├── configuration_my_custom_policy.py
        ├── modeling_my_custom_policy.py
        └── processor_my_custom_policy.py
```

### Package Configuration

Set up your `pyproject.toml`:

```toml
[project]
name = "lerobot_policy_my_custom_policy"
version = "0.1.0"
dependencies = [
    # your policy-specific dependencies
]
requires-python = ">= 3.11"

[build-system]
build-backend = # your-build-backend
requires = # your-build-system
```

## Step 2: Define the Policy Configuration

Create a configuration class that inherits from `PreTrainedConfig` and registers your policy type:

```python
# configuration_my_custom_policy.py
from dataclasses import dataclass, field
from lerobot.configs.policies import PreTrainedConfig
from lerobot.configs.types import NormalizationMode

@PreTrainedConfig.register_subclass("my_custom_policy")
@dataclass
class MyCustomPolicyConfig(PreTrainedConfig):
    """Configuration class for MyCustomPolicy.

    Args:
        n_obs_steps: Number of observation steps to use as input
        horizon: Action prediction horizon
        n_action_steps: Number of action steps to execute
        hidden_dim: Hidden dimension for the policy network
        # Add your policy-specific parameters here
    """
    # ...PreTrainedConfig fields...
    pass

    def __post_init__(self):
        super().__post_init__()
        # Add any validation logic here

    def validate_features(self) -> None:
        """Validate input/output feature compatibility."""
        # Implement validation logic for your policy's requirements
        pass
```

## Step 3: Implement the Policy Class

Create your policy implementation by inheriting from LeRobot's base `PreTrainedPolicy` class:

```python
# modeling_my_custom_policy.py
import torch
import torch.nn as nn
from typing import Dict, Any

from lerobot.policies.pretrained import PreTrainedPolicy
from .configuration_my_custom_policy import MyCustomPolicyConfig

class MyCustomPolicy(PreTrainedPolicy):
    config_class = MyCustomPolicyConfig
    name = "my_custom_policy"

    def __init__(self, config: MyCustomPolicyConfig, dataset_stats: Dict[str, Any] = None):
        super().__init__(config, dataset_stats)
        ...
```

## Step 4: Add Data Processors

Create processor functions:

```python
# processor_my_custom_policy.py
from typing import Dict, Any
import torch


def make_my_custom_policy_pre_post_processors(
    config,
) -> tuple[
    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
    PolicyProcessorPipeline[PolicyAction, PolicyAction],
]:
    """Create preprocessing and postprocessing functions for your policy."""
    pass  # Define your preprocessing and postprocessing logic here

```

## Step 5: Package Initialization

Expose your classes in the package's `__init__.py`:

```python
# __init__.py
"""Custom policy package for LeRobot."""

try:
    import lerobot  # noqa: F401
except ImportError:
    raise ImportError(
        "lerobot is not installed. Please install lerobot to use this policy package."
    )

from .configuration_my_custom_policy import MyCustomPolicyConfig
from .modeling_my_custom_policy import MyCustomPolicy
from .processor_my_custom_policy import make_my_custom_policy_pre_post_processors

__all__ = [
    "MyCustomPolicyConfig",
    "MyCustomPolicy",
    "make_my_custom_policy_pre_post_processors",
]
```

## Step 6: Installation and Usage

### Install Your Policy Package

```bash
cd lerobot_policy_my_custom_policy
pip install -e .

# Or install from PyPI if published
pip install lerobot_policy_my_custom_policy
```

### Use Your Policy

Once installed, your policy automatically integrates with LeRobot's training and evaluation tools:

```bash
lerobot-train \
    --policy.type my_custom_policy \
    --env.type pusht \
    --steps 200000
```

## Examples and Community Contributions

Check out these example policy implementations:

- [DiTFlow Policy](https://github.com/danielsanjosepro/lerobot_policy_ditflow) - Diffusion Transformer policy with flow-matching objective. Try it out in this example: [DiTFlow Example](https://github.com/danielsanjosepro/test_lerobot_policy_ditflow)

Share your policy implementations with the community! 🤗