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README.md

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+# 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** |

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