---
license: mit
task_categories:
  - robotics
tags:
  - dp3
  - 3d-diffusion-policy
  - franka
  - oakink
  - manipulation
  - grasping
---

# OakInk 362 ep — thumb+middle (baseline_3 v4 DP3 training data)

OakInk grasp episodes for **DP3 (3D-Diffusion-Policy) training** on Franka parallel-jaw.
Sim collection via `sim/run_grasp_sim_baseline3_v4.py` (UCB_Project).

## What's in here

```
data/
├── oakink__<obj_id>_<sub>__<ts>__<subj>__<cam>.hdf5             # orig yaw=0
├── oakink__<obj_id>_<sub>__<ts>__<subj>__<cam>_yaw{90,180,270}.hdf5
└── ...
RESCUE_LOG.md   # which ep are from the rescue (thumb+INDEX) layer
```

- **268 ep** collected fresh with `PINCH_FINGER=middle` (J=12)
- **94 ep** rescued from a prior thumb+INDEX (J=8) run to fill gaps where
  thumb+middle couldn't grasp — see `RESCUE_LOG.md`
- **Total: 362 ep** covering **57 / 74** use=true OakInk obj
- yaw augmentation: 3 yaws per source ep (90, 180, 270) + orig

## ⚠️ DexYCB data (162 ep) is NOT included here

**Use the existing repo:** `UCBProject/baseline_3_v4_dexycb162_oakink207_dp3`
(the DexYCB hdf5 are unchanged between this run and the prior one).

For training: concatenate both — DexYCB 162 ep + this OakInk 362 ep → **524 ep total**.

## Provenance

| | |
|---|---|
| OakInk retarget | `Baseline1/oakink/retarget_oakink.py` (UCB_Project) |
| PINCH_FINGER default | `middle` (= MANO J=12) — empirically ~2× collection rate vs `index` |
| Onset selection | `OLD` (= argmax(d ≤ d_min+4cm)) |
| Yaw aug | 3 yaws (90/180/270) at collection time |
| Sim runtime | IsaacSim 5.1 (env_isaaclab), RTX 5090, PAR=3 |
| Collector | `sim/run_grasp_sim_baseline3_v4.py` |
| Mass | 0.05 kg hardcoded (PhysX-stable) |
| cuRobo fallback | enabled (`solve_plan_with_fallback`) |
| Collection date | 2026-05-26 → 2026-05-27 |
| Wallclock | 5h53m at PAR=3 on RTX 5090 |
| Errors | 0 sanity_fail / 0 abort / 0 OOM |

## How rescue worked

Smoke comparison on A01001 showed thumb+middle had a ~2× collection rate.
After the full74 batch finished, we filled gaps from the prior thumb+INDEX
run (`Baseline1/data/episodes_b3_v4_oakink89_2026-05-26`, 207 ep) where:
- the same `(src_ep, yaw)` filename **did not exist** in the new thumb+middle run
- → copied OLD/F → NEW/F (transparent merge, same filename)

This introduces a small mixed-convention noise: rescued ep have a target EE
position ~2.8 cm offset from the new ep (different finger midpoint), but DP3
PC→EE training tolerates this for the coverage benefit. **RESCUE_LOG.md** lists
the 94 rescued files for full traceability.

## Manifest

74 use=true OakInk obj (out of 89 in `Baseline1/oakink/class_id_map.json`):
- 50 obj with thumb+middle ep
- +7 obj added by rescue → 57 total
- 17 obj 0-ep (mostly contactpose tools + S20* knives — geometry not parallel-jaw friendly)

## How to train (A6000 instructions)

```bash
# 1. Pull DexYCB 162 ep
hf download UCBProject/baseline_3_v4_dexycb162_oakink207_dp3 \
    --repo-type model  # ckpt only; DexYCB data is in DP3_DexYCB_training_data dataset

# Actually for the DexYCB hdf5:
hf download UCBProject/DP3_DexYCB_training_data --repo-type dataset --local-dir <DEX_DIR>

# 2. Pull OakInk 362 ep (this repo)
hf download UCBProject/baseline_3_v4_oakink362_thumb_middle \
    --repo-type dataset --local-dir <OAKINK_DIR>

# 3. Merge into single training dir
mkdir -p Baseline1/data/episodes_b3_v4_dexycb162_oakink362_combined
cp <DEX_DIR>/data/*.hdf5     Baseline1/data/episodes_b3_v4_dexycb162_oakink362_combined/
cp <OAKINK_DIR>/data/*.hdf5  Baseline1/data/episodes_b3_v4_dexycb162_oakink362_combined/
ls Baseline1/data/episodes_b3_v4_dexycb162_oakink362_combined/*.hdf5 | wc -l   # should be 524
```

Train with `task_baseline1_b3_v4_*.yaml` (see prior run's config for params).