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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 |