Datasets:
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)
for the next round of baseline_3 v4 Diffusion Policy 3D training.
Source code:
UCB_Project @ 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=0cam=0— single camera retained per session; OakInk's 4-camera redundancy was de-duplicated since all 4 produce bit-identical world-frame trajectories_yawDDDsuffix → yaw-augmented variant (90/180/270); no suffix → original
Download
# 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:
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)
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 "$NEW"/*.hdf5 | wc -l # expect 162 + 207 = 369
Step 2 — Build zarr (fresh, distinct from the DexYCB-only zarr)
conda activate dp3 # same dp3 env A6000 already has
# Output to a NEW zarr file (do not overwrite the existing one)
python Baseline1/convert_to_zarr.py \
"$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:
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/$EXP/data_split
Step 4 — Configure DP3 training (fresh config, output dir)
Copy the prior config and adjust:
# Copy prior config (whatever you used for the 162-ep run)
cp config/v4_sml.yaml config/${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}/${now:%Y-%m-%d_%H-%M-%S}
# - checkpoint.save_ckpt: True ← critical, defaulted False historically
# - checkpoint.topk.k: 3 (or more) ← keep last-N best
# - training.num_epochs: 3000 (matches prior run)
Step 5 — Launch training
WANDB_MODE=online # or offline if A6000 has no internet
python train.py --config-name=${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:
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.
@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}
}