peterchenyipu/cloth_dataset

Cloth-Sphere Push Datasets

Four splits of a cloth-being-pushed-over-a-sphere dataset, generated with examples/generate_cloth_sphere_push.py (see gendata.sh at repo root).

The train/test split is defined by cloth size (particles per side):

Split	Directory	Episodes	Cloth size range	Seed
Train	cloth_push_sphere/	2000	100–150 × 100–150	42
ID test	cloth_push_sphere_id_test/	100	100–150 × 100–150	88
OOD test (smaller)	cloth_push_sphere_ood_test_smaller/	50	50–100 × 50–100	42
OOD test (larger)	cloth_push_sphere_ood_test_larger/	50	150–200 × 150–200	42

All splits share the same scene setup, horizon, resolution, and action parameters.

---

Directory Layout (per split)

<split>/
├── dataset.h5        ← actions + picker positions for all episodes
└── episodes/
    ├── episode_00000.mp4
    └── ...

No observations array is stored in the HDF5 file — the MP4s are the canonical source of visual observations (hdf5+mp4 format).

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Common Parameters

Parameter	Value
Horizon (steps/episode)	64
Image resolution	512 × 512
Video FPS	16
Grab steps	3
Action repeat	16
Sphere radius	0.25 m
Sphere centre	(0.0, 0.25, 0.0) m
Num variations	20

---

HDF5 Layout

dataset.h5
├── episode_00000/
│   ├── actions          (64, 8)     float32
│   └── picker_positions (65, 2, 3)  float32
│   attrs: cloth_dimx, cloth_dimy
├── episode_00001/
│   └── ...
attrs: num_episodes, horizon, img_size, grab_steps, action_repeat,
       cloth_dimx_range, cloth_dimy_range, sphere_radius, sphere_center,
       seed, num_variations, notes

actions — (T, 8) float32

actions[t] = [dx1, dy1, dz1, grab1,  dx2, dy2, dz2, grab2]
              ←—  picker 0  —→        ←—  picker 1  —→

Field	Units	Notes
dx, dy, dz	metres / sub-step	Actual executed delta: (pos_after − pos_before) / action_repeat
grab	—	Always 1.0; cloth is held throughout

Faithful recording: the stored delta is the actual motion observed in the simulator, not the intended command. Any clamping by the physics engine is already reflected, so actions[t] exactly explains the motion in the MP4.

First grab_steps = 3 actions have dx = dy = dz = 0 (grab-only, no motion). Net picker displacement per step = delta × action_repeat (up to 16 × 0.01 = 0.16 m).

picker_positions — (T+1, 2, 3) float32

World-space XYZ in metres (Y-up). Index t is the state before actions[t]. Index T is the terminal state after the last action.

Episode attributes

Attribute	Description
cloth_dimx	Cloth width in particles (sampled from cloth_dimx_range)
cloth_dimy	Cloth height in particles (sampled from cloth_dimy_range)

---

Temporal Alignment

t=0   picker_positions[0]  →  actions[0]  →  picker_positions[1]
      video frame 0                           video frame 1
...
t=63  picker_positions[63] →  actions[63] →  picker_positions[64]
      video frame 63                          video frame 64

picker_positions[t] and video frame t are the state before actions[t]. Each MP4 has T + 1 = 65 frames (including the terminal frame).

---

Reading the Dataset

import h5py
import numpy as np

with h5py.File('data/cloth_push_sphere/dataset.h5', 'r') as f:
    horizon       = int(f.attrs['horizon'])        # 64
    action_repeat = int(f.attrs['action_repeat'])  # 16

    for ep_key in sorted(f.keys()):
        grp  = f[ep_key]
        acts = grp['actions'][:]          # (64, 8)    float32
        ppos = grp['picker_positions'][:] # (65, 2, 3) float32
        dimx = int(grp.attrs['cloth_dimx'])
        dimy = int(grp.attrs['cloth_dimy'])

        # (state_t, action_t, state_{t+1}) triples
        for t in range(horizon):
            pos_before = ppos[t]    # (2, 3)
            action     = acts[t]    # (8,)
            pos_after  = ppos[t+1]  # (2, 3)

# Load the corresponding video (frame t matches picker_positions[t])
import imageio
frames = imageio.v2.mimread('data/cloth_push_sphere/episodes/episode_00000.mp4')
# frames[t]: (512, 512, 3) uint8, pre-action frame for actions[t]

---

Storage

Split	HDF5	Videos	Total
cloth_push_sphere (train)	~11 MB	~172 MB	~183 MB
cloth_push_sphere_id_test	~0.6 MB	~8.7 MB	~9.3 MB
cloth_push_sphere_ood_test_smaller	~0.3 MB	~2.4 MB	~2.7 MB
cloth_push_sphere_ood_test_larger	~0.3 MB	~6.6 MB	~6.9 MB
Total			~202 MB