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license: mit
task_categories:
- robotics
tags:
- push-t
- diffusion-policy
- imitation-learning
- teleoperation
size_categories:
- n<1K
---
# Push-T: 200 Teleoperated Demonstrations
200 demonstrations of the 2D Push-T task (push a T-shaped block onto a fixed target pose), collected by mouse teleoperation with `demo_pusht.py` from the [Diffusion Policy](https://github.com/real-stanford/diffusion_policy) codebase. Collected for a study of how Diffusion Policy performance scales with the number of demonstrations.
- Code and write-up: https://github.com/manhasv/pusht200
- Trained checkpoints (50/100/150/200 demos): https://huggingface.co/manhasv/diffusion-policy-pusht200
## Contents
| | |
|---|---|
| Episodes | 200 (episode *i* starts from env seed *i*, seeds 0–199) |
| Total steps | 25,269 at 10 Hz |
| Episode length | 126 ± 46 steps (min 31, median 119, max 275) |
| Outcome | every episode ends in success: `demo_pusht.py` stops an episode automatically once the T covers > 95% of the target |
| Demonstrator | a single person, mouse teleoperation |
## Format
A zarr `ReplayBuffer` (the format used throughout the Diffusion Policy repo). All episodes are concatenated along the first axis; `meta/episode_ends` gives the end index of each episode.
| Key | Shape | Type | Description |
|---|---|---|---|
| `data/action` | (T, 2) | float32 | target position of the agent, in pixels (0–512) |
| `data/state` | (T, 5) | float32 | `[agent_x, agent_y, block_x, block_y, block_angle]` |
| `data/keypoint` | (T, 9, 2) | float32 | 9 keypoints of the T block, in pixels |
| `data/img` | (T, 96, 96, 3) | uint8 | rendered RGB observation |
| `data/n_contacts` | (T, 1) | float32 | number of agent–block contacts |
| `meta/episode_ends` | (200,) | int64 | cumulative episode end indices |
## Download
```bash
hf download manhasv/pusht-200-data pusht_200.zarr.tar.gz --repo-type dataset --local-dir .
mkdir -p data && tar xzf pusht_200.zarr.tar.gz -C data/ # -> data/pusht_200.zarr
```
## Notes
- This dataset is for demo purpose only. Compared with the original paper's Push-T demonstrations, this dataset have similar episode lengths but more idle frames (13% vs. 3%), jerkier motion, and ~27% longer agent paths. Policies trained on this data scored lower than the authors' results at similar dataset sizes.
## License
MIT, following the upstream Diffusion Policy repository. |