Add a dataset card; document the Franka pickplace tarball

README.md

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+---
+license: mit
+task_categories:
+- robotics
+tags:
+- point-cloud
+- world-model
+- canonical-point-cloud
+- rigid-transform
+pretty_name: 3DWM kinder training datasets
+---
+
+# 3DWM training datasets
+
+Demonstration data converted to the point-cloud dataset format used by the 3DWM
+world model, i.e. the layout its `maniskill` loader expects:
+
+```
+<dataset>/<split>/<scenario>/demo_N/
+    phases_dict.pkl      {'cannonical_points': {geom: (P, 3)}}   canonical local-frame points
+    0.h5 .. (T-1).h5     transforms/<geom> (4, 4), action (7,), names, tcp_pose (7,)
+```
+
+Points are stored once per geom in its local frame, with a 4x4 local-to-world
+transform per timestep, which gives point-to-point correspondence across time,
+clean part segmentation and complete surfaces. Velocity features are computed by
+differencing corresponding points, so these are **not** sensor point clouds and a
+raw depth capture cannot be substituted.
+
+## Contents
+
+* `StackCube-v1-demos/` — ManiSkill 3 StackCube, unpacked per-demo directories.
+* `Franka-pickplace-1000demos-v2.tar.gz` — 74 MB,
+  md5 `23decfb43157934b6a4cfa1871dac1af`. kinder MuJoCo `FrankaPickPlace3D-o1`:
+  1000 train + 10 test demos, 111,181 files, 1.3 GB unpacked. Shipped as a
+  tarball because ~110k files of ~12 KB each is a poor fit for per-file hosting.
+
+```bash
+hf download Flashkernel/3dwm-kinder-data Franka-pickplace-1000demos-v2.tar.gz \
+    --repo-type dataset --local-dir .
+tar xzf Franka-pickplace-1000demos-v2.tar.gz -C data/
+```
+
+Sanity check after extracting — part names, order and counts must match what the
+training config lists as `env_keys`:
+
+```python
+import pickle
+d = pickle.load(open('data/Franka-pickplace-1000demos-v2/train/pickplace/demo_0/phases_dict.pkl','rb'))
+pts = d['cannonical_points']
+print(list(pts.keys()))
+print([v.shape[0] for v in pts.values()])   # [300, 38,38,38,38, 37,37,37,37]
+```
+
+## Franka pickplace provenance
+
+Converted from the raw demos with
+`data_generation/kindergarden/convert_sweep_to_3dwm.py --pads 300 --cube 300`,
+which selects the gripper pads and the cube and sets their point budgets. Actions
+are normalized with `pos_scale=0.1` and `rot_scale=-0.1` (note the sign) and
+clipped to [-1, 1]; real end-effector motion is ~14 mm per step.
+
+Earlier stages of the chain — the raw kindergarden demo pickles and the packed
+canonical HDF5 — are under `franka_pickplace/` in
+[`Flashkernel/Kinder-worldmodel`](https://huggingface.co/datasets/Flashkernel/Kinder-worldmodel).
+The world model trained on this data is at
+[`Flashkernel/3dwm-franka-pickplace-mppi`](https://huggingface.co/Flashkernel/3dwm-franka-pickplace-mppi),
+whose card documents which kindergarden commit the environment must be pinned to
+(it matters, and getting it wrong fails silently).