README.md

---
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}
}
```