> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/huggingface/lerobot/llms.txt
> Use this file to discover all available pages before exploring further.

# Examples and Use Cases

> Explore example projects and practical use cases for LeRobot

LeRobot provides a comprehensive set of examples to help you get started with different features and use cases. These examples demonstrate best practices and common workflows.

## Dataset Examples

Learn how to work with LeRobot datasets.

### Loading Datasets

The `load_lerobot_dataset.py` example demonstrates:

* Viewing dataset metadata and properties
* Loading datasets from the Hugging Face Hub
* Accessing frames by episode number
* Using timestamp-based frame selection
* Batch processing with PyTorch DataLoader

```python theme={null}
from lerobot.datasets.lerobot_dataset import LeRobotDataset

# Load a dataset from the Hub
dataset = LeRobotDataset("lerobot/aloha_mobile_cabinet")

# Access data (automatically handles video decoding)
episode_index = 0
print(f"{dataset[episode_index]['action'].shape=}")
```

**Example location:** `examples/dataset/load_lerobot_dataset.py`

### Dataset Transformations

The `use_dataset_image_transforms.py` example shows:

* Applying image transformations to dataset frames
* Custom preprocessing pipelines
* Integration with PyTorch transforms

**Example location:** `examples/dataset/use_dataset_image_transforms.py`

### Dataset Tools

The `use_dataset_tools.py` example covers:

* Deleting episodes
* Splitting datasets by indices/fractions
* Adding/removing features
* Merging multiple datasets

**Example location:** `examples/dataset/use_dataset_tools.py`

## Training Examples

### Training Policies

The `train_policy.py` example demonstrates how to train a Diffusion Policy on the PushT environment:

```python theme={null}
from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
from lerobot.datasets.lerobot_dataset import LeRobotDataset

# Load dataset
dataset = LeRobotDataset("lerobot/pusht")

# Create and configure policy
config = DiffusionConfig()
policy = DiffusionPolicy(config, dataset.meta)

# Train the policy
# See full example for training loop details
```

**Example location:** `examples/training/train_policy.py`

### Streaming Training

The `train_with_streaming.py` example shows:

* Training with streaming datasets for large-scale data
* Memory-efficient loading
* Distributed training setup

**Example location:** `examples/training/train_with_streaming.py`

## Tutorial Examples

Comprehensive tutorials organized by policy type:

### ACT Policy

* **Training:** `examples/tutorial/act/act_training_example.py`
* **Inference:** `examples/tutorial/act/act_using_example.py`

### Diffusion Policy

* **Training:** `examples/tutorial/diffusion/diffusion_training_example.py`
* **Inference:** `examples/tutorial/diffusion/diffusion_using_example.py`

### VLA Models

* **Pi0:** `examples/tutorial/pi0/using_pi0_example.py`
* **SmolVLA:** `examples/tutorial/smolvla/using_smolvla_example.py`

### Reinforcement Learning

* **HIL-SERL:** `examples/tutorial/rl/hilserl_example.py`
* **Reward Classifier:** `examples/tutorial/rl/reward_classifier_example.py`

### Async Inference

* **Policy Server:** `examples/tutorial/async-inf/policy_server.py`
* **Robot Client:** `examples/tutorial/async-inf/robot_client.py`

## Real-World Use Cases

<CardGroup cols={2}>
  <Card title="Mobile Manipulation" icon="robot">
    Train mobile manipulators using the ALOHA dataset for tasks like cabinet opening and object manipulation.
  </Card>

  <Card title="Bimanual Control" icon="hands">
    Use ACT policy for bimanual tasks requiring coordinated control of two robot arms.
  </Card>

  <Card title="Vision-Language Tasks" icon="eye">
    Leverage VLA models like Pi0 or SmolVLA for language-conditioned robotic tasks.
  </Card>

  <Card title="Simulation to Real" icon="arrows-turn-to-dots">
    Train policies in simulation (PushT, LIBERO) and transfer to real robots.
  </Card>
</CardGroup>

## Additional Examples

### Backward Compatibility

Replay episodes recorded with previous calibration systems:

**Example location:** `examples/backward_compatibility/replay.py`

See the [Backward Compatibility](/resources/backward-compatibility) guide for migration details.

### Dataset Porting

Convert datasets from other formats to LeRobotDataset:

**Example location:** `examples/port_datasets/`

### Robot-Specific Examples

* **LeKiwi:** `examples/lekiwi/`
* **SO100 to SO100\_EE:** `examples/so100_to_so100_EE/`
* **Phone Teleoperation:** `examples/phone_to_so100/`
* **Real-Time Control:** `examples/rtc/`

## Running Examples

All examples can be run directly from the command line:

```bash theme={null}
# Navigate to the lerobot directory
cd lerobot

# Run an example
python examples/dataset/load_lerobot_dataset.py
```

Most examples include configurable parameters. Check the script headers for usage instructions.

## Community Examples

Explore community-contributed examples and projects:

* Browse the [Hugging Face Hub](https://huggingface.co/lerobot) for shared models and datasets
* Join the [Discord](https://discord.gg/q8Dzzpym3f) to share your projects
* Check [GitHub Discussions](https://github.com/huggingface/lerobot/discussions) for use case ideas