--- 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________[_yawDDD].hdf5 ``` - `seq_id` = `_` (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//`. **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//`) is left untouched. To re-evaluate the prior model: ```bash python eval.py --config-name= # 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} } ```