Datasets:
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license: mit
task_categories:
- robotics
tags:
- behavior-cloning
- diffusion-policy
- pusht
- bimodal
- mode-editing
size_categories:
- n<1K
---
# Push-T Controlled Bimodal Teleop Dataset
100 human-teleoperated Push-T demonstrations (50 LEFT-wrap + 50 RIGHT-wrap), collected
under a **controlled fixed initial state** so the two modes have kinematically equivalent
costs. Intended for downstream bimodal behavior-cloning and mode-editing experiments.
All demos reach `coverage >= 0.95` shapely-IoU success threshold (env-defined). Demos
were collected at 10 Hz mouse-driven teleop on the 2D pygame Push-T env from the
Diffusion Policy paper (Chi et al., RSS 2023).
## What "LEFT" vs "RIGHT" means
T starts at lower-left `(185, 327, π/4)` and must translate up-right to the green goal
silhouette at `(256, 256, π/4)`. The agent (blue circle) spawns upper-right of the T
at `(327, 185) ± 15 px`. Two equal-cost wrap paths exist:
- **LEFT** — agent wraps the **upper-left** side of the T (counter-clockwise about T's center)
- **RIGHT** — agent wraps the **lower-right** side of the T (clockwise about T's center)
Modes alternate by seed parity: even seed → LEFT, odd seed → RIGHT.
## Files
| File | Purpose |
|------|---------|
| `pusht_controlled_mixed.zarr/` | The dataset (zarr v2 directory store, 100 episodes / 8060 steps) |
| `pusht_controlled_mixed_paths.png` | All 100 agent paths, split by mode (visual sanity check) |
| `demo_pusht.py` | The patched collection script used to generate this dataset |
## Zarr layout
```
pusht_controlled_mixed.zarr/
├── data/ # 8060 timesteps total, 10 Hz
│ ├── state (8060, 5) float32 # [agent_x, agent_y, T_x, T_y, T_angle]
│ ├── action (8060, 2) float32 # teleop mouse-target action
│ ├── img (8060, 96, 96, 3) uint8 # 96×96 RGB env render
│ ├── keypoint (8060, 9, 2) float32 # 9 T-block keypoints
│ └── n_contacts (8060, 1) float32
└── meta/ # 100 episodes
├── episode_ends (100,) int64 # cumulative end-indices (DP standard)
├── episode_modes (100,) int8 # 0 = LEFT, 1 = RIGHT ← extra
└── episode_coverages (100,) float32 # final IoU coverage ← extra
```
The `data/*` arrays and `meta/episode_ends` follow the **same layout** as the canonical
`pusht_cchi_v7_replay.zarr` from the DP repo. The two extra `meta/*` arrays are
additions for mode-editing work; the original DP training code ignores them.
## Loading
```python
import zarr, numpy as np
from huggingface_hub import snapshot_download
local_dir = snapshot_download(
repo_id='haohw/pusht-controlled-mixed',
repo_type='dataset',
)
r = zarr.open(f'{local_dir}/pusht_controlled_mixed.zarr', mode='r')
ends = np.asarray(r['meta/episode_ends'])
modes = np.asarray(r['meta/episode_modes']) # 0=LEFT, 1=RIGHT
covs = np.asarray(r['meta/episode_coverages'])
state = np.asarray(r['data/state'])
action = np.asarray(r['data/action'])
# Per-episode iteration
starts = np.concatenate([[0], ends[:-1]])
for s, e, m in zip(starts, ends, modes):
ep_state = state[s:e]
ep_action = action[s:e]
mode_name = 'LEFT' if m == 0 else 'RIGHT'
...
```
For fast pulls on a cluster, set `HF_HUB_ENABLE_HF_TRANSFER=1` and install `hf_transfer`.
## Splitting into per-mode zarrs
If downstream code expects two single-mode zarr files
(`pusht_controlled_left.zarr` + `pusht_controlled_right.zarr`), the split is just a
filter on `meta/episode_modes`. The original DP collection guide's two-file format
can be reconstructed from this single mixed file at training time.
## Collection spec
**Env**: `PushTKeypointsEnv` from `diffusion_policy.env.pusht.pusht_keypoints_env`,
render_size=96, control_hz=10.
**Per-episode init** (overrides env's random init via `env.reset_to_state`):
| Object | Position | Angle |
|--------|----------|-------|
| T block | fixed `(185, 327)` | fixed `π/4` |
| Goal | fixed `(256, 256)` (env default) | fixed `π/4` |
| Agent | `(327, 185) + uniform(-15, +15)` (seeded by episode index) | — |
**Success**: shapely-IoU between current T polygon and goal T polygon `>= 0.95`.
DP paper reports continuous max-coverage per episode (clipped at 0.95), not binary
success rate — so 95% is the env's done flag, not the eval metric.
## Episode-length stats
Mean 80.6 steps (≈8.1 s @ 10 Hz), median 78, min 60, max 141.
## Reproducing
See `demo_pusht.py`. Key changes from the stock DP script:
1. Controlled init via `env.reset_to_state` (T fixed, agent perturbed ±15 px)
2. Mode auto-assigned by seed parity, displayed in-window (red/blue overlay + IoU bar)
3. Extra `meta/episode_modes` and `meta/episode_coverages` saved per episode
4. `S` key to save partial demos before 95%, `C` key to cancel and restart same seed
## License
MIT. Re-uses geometry / env from
[real-stanford/diffusion_policy](https://github.com/real-stanford/diffusion_policy).
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