---
license: apache-2.0
task_categories:
  - robotics
  - reinforcement-learning
tags:
  - robotics
  - imitation-learning
  - diffusion-policy
  - manipulation
  - fetch
  - mujoco
  - lerobot
size_categories:
  - 1K<n<10K
---

# DiffPick: Fetch Pick-and-Place Demonstrations

A clean dataset of **200 successful pick-and-place demonstrations** collected from a scripted expert policy in the [`FetchPickAndPlace-v4`](https://robotics.farama.org/envs/fetch/pick_and_place/) MuJoCo environment. Designed for training **vision-based imitation learning** policies (Diffusion Policy, ACT, BC).

Part of the [DiffPick project](https://github.com/e-cagan/diffpick) — a from-scratch implementation of a Diffusion Policy pipeline with ROS2 deployment.

## Dataset Stats

| Property | Value |
|---|---|
| Episodes | 200 |
| Total frames | 5,489 |
| Mean episode length | 27.4 steps |
| Min / Max length | 20 / 35 steps |
| FPS | 25 |
| Image resolution | 96×96 RGB |
| Success rate (during collection) | 97.1% (200 of 206 attempts kept) |

## Features

| Key | Shape | Type | Description |
|---|---|---|---|
| `observation.image` | (3, 96, 96) | float32 | Front-view RGB camera |
| `observation.state` | (10,) | float32 | Robot proprioception only (gripper xyz, finger widths, velocities). **No object pose** — must be inferred from image. |
| `action` | (4,) | float32 | End-effector delta (dx, dy, dz) ∈ [-1,1] + gripper command (-1 close, +1 open) |
| `task` | string | — | "Pick up the block and place it at the target location." |

### Why proprioception-only state?

The state vector deliberately **excludes object position**. This forces a learned policy to develop visual grounding rather than copying ground-truth coordinates. The result: policies trained on this dataset must actually *see* the object in the RGB stream to succeed — closer to a real-world deployment scenario where object pose isn't directly observable.

## Expert Policy

Demonstrations were generated by a hand-crafted state machine controller:

```
APPROACH (gripper open, hover above object)
↓
DESCEND (gripper open, lower to object)
↓
GRASP (close gripper, hold for 8 steps)
↓
PLACE (move to target, gripper closed)
```

Proportional control in end-effector space (no IK required, since the env exposes a 4-D end-effector action interface). Episodes ending in success too quickly (< 15 steps, indicating object near target at reset) were filtered out.

Source: [`data_collection/scripted_policy.py`](https://github.com/e-cagan/diffpick/blob/main/data_collection/scripted_policy.py)

## Usage

```python
from lerobot.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("e-cagan/diffpick")
sample = dataset[0]

print(sample["observation.image"].shape)  # torch.Size([3, 96, 96])
print(sample["observation.state"].shape)  # torch.Size([10])
print(sample["action"].shape)             # torch.Size([4])
```

## Reproduction

```bash
git clone https://github.com/e-cagan/diffpick
cd diffpick
pip install -r requirements.txt

# Collect raw demos
python -m data_collection.collect --n_episodes 200

# Convert to LeRobotDataset format
python -m data_collection.to_lerobot_dataset \
    --raw_dir data/raw_demos \
    --repo_id <your-username>/diffpick \
    --fps 25
```

## Intended Use

- Training **Diffusion Policy** for vision-conditioned manipulation
- Benchmarking imitation learning algorithms (BC vs ACT vs DP)
- Learning resource for ROS2 + MuJoCo + LeRobot integration

## Limitations

- Single environment seed family (`FetchPickAndPlace-v4` defaults). No domain randomization for backgrounds, lighting, or distractors.
- Single front-facing 96×96 camera. No wrist cam, no depth.
- Scripted expert is deterministic given a seed — no behavioral diversity (no left-hand/right-hand approach modes, etc.). This may limit the multi-modal advantages of Diffusion Policy.
- Object is a single blue cube. No category generalization.

## Citation

If you use this dataset, please cite:

```bibtex
@misc{apaydin2026diffpick,
  author = {Apaydın, Emin Çağan},
  title = {DiffPick: A Diffusion Policy Pipeline for Fetch Pick-and-Place},
  year = {2026},
  publisher = {GitHub},
  url = {https://github.com/e-cagan/diffpick}
}
```

## License

Apache 2.0