---
datasets: Eshwar-2123/pick_place_blue_cube_box_clean_v1_20260706_125359
library_name: lerobot
license: apache-2.0
model_name: diffusion
pipeline_tag: robotics
tags:
- lerobot
- robotics
- diffusion
---

# Model Card for Diffusion Policy

[Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://huggingface.co/papers/2303.04137) is an imitation learning policy that models robot actions as a denoising diffusion process. It is particularly effective for learning multi-modal manipulation behaviors from demonstrations while producing smooth action trajectories.

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Add a demo GIF after benchmarking.

<p align="center">
  <img src="https://huggingface.co/Eshwar-2123/diffusion_pick_place_clean/resolve/main/demo.gif" width="60%"/>
</p>
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This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).

Learn how to train and run it in the [LeRobot Diffusion Policy guide](https://huggingface.co/docs/lerobot/main/en/diffusion), or browse the [full documentation](https://huggingface.co/docs/lerobot/index).

---

# Model Details

- **License:** apache-2.0
- **Robot type:** `so_follower`
- **Cameras:** `front`, `wrist`

## Inputs & Outputs

The policy consumes these observation features and produces these action features.

### Inputs

| Feature | Type | Shape |
| --- | --- | --- |
| `observation.state` | STATE | `(6,)` |
| `observation.images.front` | VISUAL | `(3, 480, 640)` |
| `observation.images.wrist` | VISUAL | `(3, 480, 640)` |

### Outputs

| Feature | Type | Shape |
| --- | --- | --- |
| `action` | ACTION | `(6,)` |

---

# Training Dataset

- **Repository:** [Eshwar-2123/pick_place_blue_cube_box_clean_v1_20260706_125359](https://huggingface.co/datasets/Eshwar-2123/pick_place_blue_cube_box_clean_v1_20260706_125359)
- **Episodes:** 47
- **Frames:** 20321
- **Frame rate:** 30 FPS
- **Task(s):** ""

<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=Eshwar-2123/pick_place_blue_cube_box_clean_v1_20260706_125359">
<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
</a>

---

# Training Configuration

| Setting | Value |
| --- | --- |
| Training steps | 30000 |
| Batch size | 16 |
| Optimizer | adamw |
| Learning rate | 1e-04 |
| Seed | 1000 |
| LeRobot version | 0.5.2 |

---

# How to Get Started with the Model

New to LeRobot? These guides cover the full workflow:

- **[Install LeRobot](https://huggingface.co/docs/lerobot/main/en/installation)** — set up the `lerobot` package.
- **[Hardware setup](https://huggingface.co/docs/lerobot/main/en/hardware_guide)** — assemble, wire, and calibrate your robot and cameras.
- **[Record data & train a policy](https://huggingface.co/docs/lerobot/en/il_robots)** — the end-to-end imitation-learning walkthrough.
- **[CLI cheat-sheet](https://huggingface.co/docs/lerobot/main/en/cheat-sheet)** — quick reference for the `lerobot-*` commands.

## Run the policy on your robot

```bash
lerobot-rollout \
  --strategy.type=base \
  --robot.type=so_follower \
  --robot.port=<your_robot_port> \
  --robot.cameras="{ <camera_1>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}, <camera_2>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}}" \
  --policy.path=Eshwar-2123/diffusion_pick_place_clean \
  --task="" \
  --duration=60
```

Replace the remaining `<...>` placeholders with your own values.

---

## Train your own policy

```bash
lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.type=diffusion \
  --output_dir=outputs/train/<policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<policy_repo_id> \
  --wandb.enable=true
```

_Writes checkpoints to `outputs/train/<policy_repo_id>/checkpoints/`._

---

# Evaluation

_No evaluation results have been provided for this policy yet._

---

# Citation

If you use this policy, please cite **Diffusion Policy**, along with **LeRobot**.

```bibtex
@article{chi2023diffusionpolicy,
  title={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
  author={Chi, Cheng and Feng, Siyuan and Du, Yilun and Xu, Zheng and Cousineau, Eric and Burchfiel, Benjamin and Song, Shuran},
  journal={RSS},
  year={2023}
}

@misc{cadene2024lerobot,
    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
    title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
    howpublished = "\url{https://github.com/huggingface/lerobot}",
    year = {2024}
}
```