--- 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: ``` ///demo_N/ phases_dict.pkl {'cannonical_points': {geom: (P, 3)}} canonical local-frame points 0.h5 .. (T-1).h5 transforms/ (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).