Add dataset card (schema, per-object breakdown, download)

README.md

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+---
+license: cc-by-4.0
+language:
+- en
+pretty_name: baseline_3 v4 - DP3 Training Trajectories (DexYCB-sourced)
+tags:
+- robotics
+- manipulation
+- grasping
+- diffusion-policy
+- franka
+- dexycb
+size_categories:
+- n<1K
+---
+
+# DP3_DexYCB_training_data
+
+162 successful grasp + lift trajectories collected in IsaacSim 5.1 + cuRobo
+0.8 from DexYCB hand-pose sequences, retargeted onto a Franka 2-finger
+gripper. Used to train the **baseline_3 v4** Diffusion Policy 3D (DP3)
+policy in the
+[UCB_Project](https://github.com/stzabl-png/UCB_Project) repo
+(`gate3-curobo-ik` branch).
+
+10 YCB objects after dropping `foam` and `scissors` (cuRobo could not plan a
+single successful grasp on either shape). Each source DexYCB episode was
+collected at its original object yaw and one randomly-selected augmented
+yaw in `{90°, 180°, 270°}` around world-Z (a task-symmetric transform —
+gravity, table and contact geometry are unchanged by yaw rotation).
+
+## Per-object Breakdown
+
+| ycb_class_id | object       | orig | yaw aug | total |
+|--------------|--------------|------|---------|-------|
+| 03           | sugar        | 14   | 8       | 22    |
+| 04           | tomato       | 14   | 6       | 20    |
+| 05           | mustard      | 11   | 4       | 15    |
+| 06           | tuna         | 11   | 6       | 17    |
+| 07           | pudding      | 9    | 9       | 18    |
+| 08           | gelatin      | 11   | 4       | 15    |
+| 09           | potted_meat  | 11   | 7       | 18    |
+| 12           | bleach       | 17   | 8       | 25    |
+| 15           | drill        | 3    | 4       | 7     |
+| 18           | marker       | 3    | 2       | 5     |
+|              | **TOTAL**    | 104  | 58      | **162** |
+
+Total size: ~238 MB. Each `.hdf5` is ~1.5 MB.
+
+## Per-episode Schema (HDF5)
+
+| Field              | Shape          | dtype   | Notes |
+|--------------------|----------------|---------|-------|
+| `state`            | `(31, 8)`      | float32 | `[x,y,z, qw,qx,qy,qz, gripper]` in object-centric G-frame, retarget-quat convention |
+| `action`           | `(31, 8)`      | float32 | `state[1:]` (shifted by 1) |
+| `point_cloud`      | `(31, 4096, 3)`| float32 | Static CAD surface samples in G-frame (all 31 frames identical; object is static during collection) |
+| `obj_origin_G`     | attr `(3,)`    | float64 | Object frame origin in G-frame |
+| `obj_quat_G_wxyz`  | attr `(4,)`    | float64 | Object orientation in G-frame |
+| `ycb_class_id`     | attr scalar    | int     | DexYCB class id (e.g. 03 = sugar) |
+
+## File Naming
+
+```
+dexycb__<session>__<sub-session>__<camera_id>__ycb_dex_NN[_yawDDD].hdf5
+```
+- No `_yaw` suffix → original DexYCB yaw
+- `_yaw90` / `_yaw180` / `_yaw270` → yaw-augmented variant
+
+## Download
+
+```bash
+# Option 1: huggingface-cli
+huggingface-cli download UCBProject/DP3_DexYCB_training_data \
+    --repo-type dataset \
+    --local-dir Baseline1/data/episodes_b3_v4_full12_yaw
+
+# Option 2: snapshot_download from Python
+from huggingface_hub import snapshot_download
+snapshot_download(repo_id="UCBProject/DP3_DexYCB_training_data",
+                  repo_type="dataset",
+                  local_dir="Baseline1/data/episodes_b3_v4_full12_yaw")
+```
+
+The 162 hdf5 files land under `data/` inside this repo, so the
+`--local-dir` above ends up with `Baseline1/data/episodes_b3_v4_full12_yaw/data/<162 hdf5>`.
+For the UCB_Project pipeline, move them up one level so the path matches the
+README:
+
+```bash
+mv Baseline1/data/episodes_b3_v4_full12_yaw/data/*.hdf5 \
+   Baseline1/data/episodes_b3_v4_full12_yaw/
+rmdir Baseline1/data/episodes_b3_v4_full12_yaw/data
+```
+
+## Training Pipeline
+
+Full retrain instructions:
+[`Baseline1/RETRAIN_V4_FULL12.md`](https://github.com/stzabl-png/UCB_Project/blob/gate3-curobo-ik/Baseline1/RETRAIN_V4_FULL12.md)
+in the UCB_Project repo.
+
+## Collection Details
+
+Collected by `sim/run_grasp_sim_baseline3_v4.py` (`gate3-curobo-ik` branch)
+with:
+- IsaacSim 5.1, PhysX TGS solver, GPU dynamics + CCD
+- cuRobo 0.8 motion planner (per-phase mesh toggle for pre-grasp WITH mesh,
+  final/lift WITHOUT mesh)
+- mass = 0.05 kg (hardcoded; real per-class mass triggers PhysX overflow)
+- chunked-5 + retry wrapper to recover from PhysX corruption events
+
+## License
+
+Data: CC-BY-4.0. DexYCB source data subject to the original DexYCB license.
+
+## Citation
+
+If you use this dataset, please also cite DexYCB:
+```
+@inproceedings{chao2021dexycb,
+  title = {DexYCB: A Benchmark for Capturing Hand Grasping of Objects},
+  author = {Chao, Yu-Wei and Yang, Wei and Xiang, Yu and Molchanov, Pavlo
+            and Handa, Ankur and Tremblay, Jonathan and Narang, Yashraj S
+            and Van Wyk, Karl and Iqbal, Umar and Birchfield, Stan and others},
+  booktitle = {CVPR},
+  year = {2021}
+}
+```