File size: 4,874 Bytes
bc9099e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | # 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).
---
## 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
```python
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** |
|