Datasets:

haohw
/

pusht-controlled-mixed

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

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:

Controlled init via env.reset_to_state (T fixed, agent perturbed ±15 px)
Mode auto-assigned by seed parity, displayed in-window (red/blue overlay + IoU bar)
Extra meta/episode_modes and meta/episode_coverages saved per episode
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.