Initial upload: 207 OakInk-sourced DP3 training trajectories (45 obj)

.gitattributes

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README.md

@@ -0,0 +1,339 @@
+---
+license: cc-by-4.0
+language:
+- en
+pretty_name: baseline_3 v4 - DP3 Training Trajectories (OakInk-sourced)
+tags:
+- robotics
+- manipulation
+- grasping
+- diffusion-policy
+- franka
+- oakink
+size_categories:
+- n<1K
+---
+
+# DP3_OakInk_training_data
+
+207 successful grasp + lift trajectories collected in IsaacSim 5.1 + cuRobo
+0.8 from **OakInk** hand-pose sequences, retargeted onto a Franka 2-finger
+gripper. Used as an **add-on** to the DexYCB-sourced training set
+([`UCBProject/DP3_DexYCB_training_data`](https://huggingface.co/datasets/UCBProject/DP3_DexYCB_training_data))
+for the next round of `baseline_3 v4` Diffusion Policy 3D training.
+
+Source code:
+[`UCB_Project @ gate3-curobo-ik`](https://github.com/stzabl-png/UCB_Project/tree/gate3-curobo-ik)
+— collector `sim/run_grasp_sim_baseline3_v4.py`, retarget
+`Baseline1/oakink/retarget_oakink.py`, orchestrator
+`scripts/baseline_3_v4/oakink_full89_queue_resume.sh`.
+
+**Important**: this dataset is meant to be **combined** with the
+DexYCB-sourced 162-ep set on the training side. See the "Combined Training on
+A6000" section below for the **new output paths** that must be used so the
+previous DexYCB-only checkpoint and train/test split on the A6000 are
+preserved (we still plan to evaluate the previous model).
+
+---
+
+## Why OakInk (not just more DexYCB)
+
+DexYCB has 21 (mostly food-box / can / drill) objects. OakInk adds 89 (mostly
+container / mug / tool) categories with hand annotations recorded across
+multiple subjects and grasp styles. Adding OakInk to the DP3 training set
+increases category and grasp-style diversity well beyond what DexYCB alone
+provides.
+
+The collection on RTX 5090 ran 89 objects × 846 source sessions × 4 yaws =
+3 384 attempts in ~6 h 20 min wall time. Final yield: **207 successful
+trajectories** (6.16 % overall) — lower than DexYCB's ~25 % because OakInk
+contains many wide containers (min-dim > Franka 8 cm span) and very thin
+tools (aspect-ratio > 5) that are physically un-graspable by a 2-finger
+parallel gripper. 45 of the 89 objects contributed ≥1 trajectory; 44
+contributed 0 (see "Per-object Breakdown" below).
+
+---
+
+## Per-object Breakdown
+
+45 objects with ≥1 successful trajectory:
+
+| obj_id  | cid  | category    | orig | yaw aug | total |
+|---------|------|-------------|------|---------|-------|
+| C03001  | 1058 | container   |    4 |      10 |    14 |
+| O01000  | 1001 | container   |    1 |      10 |    11 |
+| A01009  | 1012 | container   |    0 |      10 |    10 |
+| A01002  | 1008 | container   |    0 |       9 |     9 |
+| A01005  | 1011 | container   |    1 |       8 |     9 |
+| A01010  | 1009 | container   |    1 |       8 |     9 |
+| A01026  | 1072 | container   |    0 |       9 |     9 |
+| A01001  | 1003 | container   |    2 |       6 |     8 |
+| S16001  | 1017 | container   |    2 |       6 |     8 |
+| S16002  | 1039 | container   |    3 |       5 |     8 |
+| A02015  | 1015 | container   |    2 |       5 |     7 |
+| A15027  | 1025 | container   |    0 |       7 |     7 |
+| A01008  | 1007 | container   |    0 |       6 |     6 |
+| A01023  | 1071 | container   |    1 |       5 |     6 |
+| C14001  | 1050 | container   |    1 |       5 |     6 |
+| Y27035  | 1020 | maniptools  |    0 |       6 |     6 |
+| A02021  | 1037 | container   |    2 |       3 |     5 |
+| O03001  | 1014 | container   |    0 |       5 |     5 |
+| S16005  | 1036 | container   |    0 |       5 |     5 |
+| A02011  | 1022 | container   |    1 |       3 |     4 |
+| O03003  | 1029 | container   |    0 |       4 |     4 |
+| O21001  | 1044 | maniptools  |    1 |       3 |     4 |
+| A01027  | 1073 | container   |    0 |       3 |     3 |
+| C10001  | 1076 | container   |    0 |       3 |     3 |
+| C22001  | 1055 | maniptools  |    1 |       2 |     3 |
+| C37001  | 1056 | maniptools  |    1 |       2 |     3 |
+| O02001  | 1021 | container   |    1 |       2 |     3 |
+| S15004  | 1032 | container   |    2 |       1 |     3 |
+| S20005  | 1041 | maniptools  |    0 |       3 |     3 |
+| Y03021  | 1023 | container   |    0 |       3 |     3 |
+| A02012  | 1033 | container   |    0 |       2 |     2 |
+| A02032  | 1026 | container   |    0 |       2 |     2 |
+| A15015  | 1027 | container   |    0 |       2 |     2 |
+| C42001  | 1060 | wearable    |    0 |       2 |     2 |
+| C90001  | 1078 | geometry    |    0 |       2 |     2 |
+| O03002  | 1018 | container   |    0 |       2 |     2 |
+| S10017  | 1087 | container   |    1 |       1 |     2 |
+| S16003  | 1049 | container   |    0 |       2 |     2 |
+| A02014  | 1024 | container   |    0 |       1 |     1 |
+| A02030  | 1031 | container   |    1 |       0 |     1 |
+| A16026  | 1019 | container   |    0 |       1 |     1 |
+| C15001  | 1067 | container   |    1 |       0 |     1 |
+| S10005  | 1081 | container   |    0 |       1 |     1 |
+| S10008  | 1006 | container   |    0 |       1 |     1 |
+| S10021  | 1002 | container   |    1 |       0 |     1 |
+|         |      | **TOTAL**   |   31 |     176 | **207** |
+
+`class_id` 1001–1089 corresponds to the OakInk slot in
+`Baseline1/oakink/class_id_map.json` (DexYCB occupies cid 1–21; cid > 1000
+is reserved for OakInk).
+
+Yaw augmentation contributes **85 %** of the data (176/207). For many
+objects MANO-derived grasp poses fail Franka kinematic reachability at the
+recorded orientation but succeed at one of the 90°/180°/270° rotated
+variants.
+
+---
+
+## Per-episode Schema (HDF5)
+
+Identical to `DP3_DexYCB_training_data`:
+
+| Field              | Shape          | dtype   | Notes |
+|--------------------|----------------|---------|-------|
+| `state`            | `(T, 8)`       | float32 | `[x,y,z, qw,qx,qy,qz, gripper]` in object-centric G-frame |
+| `action`           | `(T, 8)`       | float32 | `state[1:]` (shifted by 1) |
+| `point_cloud`      | `(T, 4096, 3)` | float32 | Static CAD surface samples in G-frame |
+| `obj_origin_G`     | attr `(3,)`    | float64 | `(0, 0, obj_z)` — table-relative offset |
+| `obj_quat_G_wxyz`  | attr `(4,)`    | float64 | Obj orientation in G-frame |
+| `ycb_class_id`     | attr scalar    | int64   | 1001+ for OakInk |
+| `obj_id`           | attr str       | -       | e.g. `"A01001"` |
+| `dataset`          | attr str       | -       | `"oakink"` (lets training code disambiguate) |
+
+`T` ≈ 31 frames per episode (4 hover waypoints + 12 approach + 6 grasp + 9
+lift, ±a few). `state[T-1]` is the post-lift gripper pose.
+
+---
+
+## File Naming
+
+```
+oakink__<seq_id>__<ts>__<subj_flag>__<cam>[_yawDDD].hdf5
+```
+
+- `seq_id` = `<obj_id>_<trial_indices>` (e.g. `A01001_0001_0000`)
+- `ts` = recording timestamp (`2021-09-26-19-59-58`)
+- `subj_flag` = `0` (primary) — all 207 saved episodes are from subj=0
+- `cam` = `0` — single camera retained per session; OakInk's 4-camera
+  redundancy was de-duplicated since all 4 produce bit-identical world-frame
+  trajectories
+- `_yawDDD` suffix → yaw-augmented variant (90/180/270); no suffix → original
+
+---
+
+## Download
+
+```bash
+# Option 1: huggingface-cli (recommended, parallel)
+huggingface-cli download UCBProject/DP3_OakInk_training_data \
+    --repo-type dataset \
+    --local-dir Baseline1/data/episodes_b3_v4_oakink89_2026-05-26 \
+    --include "data/*.hdf5"
+
+# Move from data/ subdir up to root
+mv Baseline1/data/episodes_b3_v4_oakink89_2026-05-26/data/*.hdf5 \
+   Baseline1/data/episodes_b3_v4_oakink89_2026-05-26/
+rmdir Baseline1/data/episodes_b3_v4_oakink89_2026-05-26/data
+```
+
+Or via Python:
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(repo_id="UCBProject/DP3_OakInk_training_data",
+                  repo_type="dataset",
+                  local_dir="Baseline1/data/episodes_b3_v4_oakink89_2026-05-26",
+                  allow_patterns="data/*.hdf5")
+```
+
+---
+
+## Combined Training on A6000 (DexYCB + OakInk → new DP3 model)
+
+**Important constraints**:
+- The A6000 already has the previous DexYCB-only DP3 checkpoint and the
+  162-ep train/test split saved on disk. We still plan to **evaluate the
+  previous model**, so the new run **MUST NOT** overwrite those paths.
+- Sim collection on A6000 was abandoned earlier this round due to
+  glibc 2.31 (system) vs 2.35 (IsaacSim 5.1 requirement) mismatch. **Do not
+  attempt sim collection on A6000.** All collection now happens on the dev
+  box (RTX 5090); A6000 is training-only.
+
+### Step 1 — Layout the combined dataset (use a fresh dir)
+
+```bash
+cd $HOME/UCB_Project   # the A6000 repo clone
+
+# Pick a FRESH name that does NOT collide with the prior run's
+# episodes_b3_v4_full12_yaw/ (which holds the 162 DexYCB ep and is the
+# input to the existing model). Suggested:
+NEW=Baseline1/data/episodes_b3_v4_dexycb162_oakink207_2026-05-26
+mkdir -p "$NEW"
+
+# 1.1 Copy DexYCB 162 ep from the existing local dir (already downloaded
+# from UCBProject/DP3_DexYCB_training_data — do NOT re-download).
+cp Baseline1/data/episodes_b3_v4_full12_yaw/*.hdf5 "$NEW/"
+
+# 1.2 Download the new OakInk 207 ep from THIS dataset.
+huggingface-cli download UCBProject/DP3_OakInk_training_data \
+    --repo-type dataset --local-dir /tmp/oakink_dl --include "data/*.hdf5"
+cp /tmp/oakink_dl/data/*.hdf5 "$NEW/"
+
+# 1.3 Verify count
+ls "$NEW"/*.hdf5 | wc -l       # expect 162 + 207 = 369
+```
+
+### Step 2 — Build zarr (fresh, distinct from the DexYCB-only zarr)
+
+```bash
+conda activate dp3   # same dp3 env A6000 already has
+
+# Output to a NEW zarr file (do not overwrite the existing one)
+python Baseline1/convert_to_zarr.py \
+    "$NEW" \
+    --output_zarr Baseline1/data/dp3_train_v4_dexycb162_oakink207.zarr
+```
+
+### Step 3 — Create a new DP3 train/test split
+
+The previous split (32 train + 8 test for the 3-obj experiment, OR 130 train
++ 32 test for the 162-ep v4_sml run) lives under
+`third_party/3D-Diffusion-Policy/.../experiments/<old_exp_name>/`.
+**Do not touch it.** Make a fresh experiment dir:
+
+```bash
+cd third_party/3D-Diffusion-Policy/3D-Diffusion-Policy
+
+# Suggested fresh experiment name
+EXP=dexycb162_oakink207_2026-05-26
+
+# 80/20 split — 295 train / 74 test (Baseline1/split_v4_full12.py is the
+# splitter used previously; copy + rerun on the NEW zarr file)
+python ../../../Baseline1/split_v4_full12.py \
+    --zarr ../../../Baseline1/data/dp3_train_v4_dexycb162_oakink207.zarr \
+    --train_ratio 0.8 \
+    --out_dir experiments/$EXP/data_split
+```
+
+### Step 4 — Configure DP3 training (fresh config, output dir)
+
+Copy the prior config and adjust:
+```bash
+# Copy prior config (whatever you used for the 162-ep run)
+cp config/v4_sml.yaml config/${EXP}.yaml
+
+# Edit config/${EXP}.yaml:
+#   - task.dataset_zarr_path:  point to the NEW zarr (dp3_train_v4_dexycb162_oakink207.zarr)
+#   - exp_name:                 ${EXP}
+#   - hydra.run.dir:            experiments/${EXP}/${now:%Y-%m-%d_%H-%M-%S}
+#   - checkpoint.save_ckpt:     True            ← critical, defaulted False historically
+#   - checkpoint.topk.k:        3 (or more)     ← keep last-N best
+#   - training.num_epochs:      3000 (matches prior run)
+```
+
+### Step 5 — Launch training
+
+```bash
+WANDB_MODE=online   # or offline if A6000 has no internet
+python train.py --config-name=${EXP}
+```
+
+Expected wall time on A6000 at batch_size=128: **~6 h** for 3000 epochs on
+369 ep (vs ~3 h for 162 ep).
+
+### Step 6 — After training, ckpt + logs land in a fresh dir
+
+```
+experiments/${EXP}/{date_time}/checkpoints/
+experiments/${EXP}/{date_time}/wandb/
+```
+
+The PRIOR experiment dir (`experiments/<prior_v4_sml_exp>/`) is left
+untouched. To re-evaluate the prior model:
+```bash
+python eval.py --config-name=<prior_v4_sml_exp>   # unchanged from before
+```
+
+---
+
+## Collection Details (same as DexYCB)
+
+- IsaacSim 5.1, PhysX TGS solver, GPU dynamics + CCD
+- cuRobo 0.8 motion planner (per-phase mesh toggle: pre-grasp WITH mesh,
+  final/lift WITHOUT mesh)
+- mass = 0.05 kg (hardcoded; real per-class mass causes PhysX overflow with
+  OakInk's photogrammetry-derived non-watertight meshes)
+- chunked-5 + retry wrapper to recover from PhysX corruption events
+- Yaw augmentation: orig + {90°, 180°, 270°} = 4 attempts per source ep
+- 2-parallel orchestrator on RTX 5090 (verified safe); 4-par broke
+  historically due to cuRobo subprocess contention
+
+---
+
+## Why the 44 zero-yield objects?
+
+Bucketed analysis on the 37 zero-yield objects in the partner shortlist
+(+ 7 self-identified):
+- **12 obj**: `min_dim ≥ 8 cm` → exceeds Franka 8 cm gripper span (e.g.
+  S10010-S10020 mug body, A02018 teapot, C13001 large kettle)
+- **10 obj**: `aspect_ratio ≥ 5` → extremely thin (e.g. S20021 spoon,
+  Y29040 stick, O24001 spatula) — gripper can't pinch
+- **6 obj**: severe mesh degeneracy (non-manifold edges > 2 000) where
+  cuRobo's collision approximation diverges from PhysX runtime geometry
+- **~9 obj**: MANO grasp pose puts the wrist below or to the side of the
+  object such that the Franka 7-DoF cannot match the required wrist
+  orientation (gripper z-axis world component > +0.2)
+
+This is **not a pipeline bug** — DexYCB succeeds at ~25 % on the same
+collector because YCB benchmark obj are designed for parallel-jaw grasping.
+The OakInk objects expose the natural human-vs-Franka kinematic gap.
+
+---
+
+## License & citation
+
+Data: CC-BY-4.0. OakInk source data subject to the original
+[OakInk license](https://oakink.net).
+
+```
+@inproceedings{yang2022oakink,
+  title = {OakInk: A Large-scale Knowledge Repository for Understanding
+           Hand-Object Interaction},
+  author = {Yang, Lixin and Li, Kailin and Zhan, Xinyu and Wu, Fei and Xu,
+            Anran and Liu, Liu and Lu, Cewu},
+  booktitle = {CVPR},
+  year = {2022}
+}
+```

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