README.md

---
license: cc-by-4.0
language:
- en
pretty_name: baseline_3 v4 - DP3 Training Trajectories (DexYCB-sourced)
tags:
- robotics
- manipulation
- grasping
- diffusion-policy
- franka
- dexycb
size_categories:
- n<1K
---

# DP3_DexYCB_training_data

162 successful grasp + lift trajectories collected in IsaacSim 5.1 + cuRobo
0.8 from DexYCB hand-pose sequences, retargeted onto a Franka 2-finger
gripper. Used to train the **baseline_3 v4** Diffusion Policy 3D (DP3)
policy in the
[UCB_Project](https://github.com/stzabl-png/UCB_Project) repo
(`gate3-curobo-ik` branch).

10 YCB objects after dropping `foam` and `scissors` (cuRobo could not plan a
single successful grasp on either shape). Each source DexYCB episode was
collected at its original object yaw and one randomly-selected augmented
yaw in `{90°, 180°, 270°}` around world-Z (a task-symmetric transform —
gravity, table and contact geometry are unchanged by yaw rotation).

> **2026-05-26 update** — A complementary OakInk-sourced dataset is now available
> at [`UCBProject/DP3_OakInk_training_data`](https://huggingface.co/datasets/UCBProject/DP3_OakInk_training_data)
> (207 ep, 45 obj). For the next DP3 round we will train on the **combined
> 369-ep dataset** (DexYCB 162 + OakInk 207). See the
> "Combined Training (DexYCB + OakInk → fresh DP3 model)" section below.

## Per-object Breakdown

| ycb_class_id | object       | orig | yaw aug | total |
|--------------|--------------|------|---------|-------|
| 03           | sugar        | 14   | 8       | 22    |
| 04           | tomato       | 14   | 6       | 20    |
| 05           | mustard      | 11   | 4       | 15    |
| 06           | tuna         | 11   | 6       | 17    |
| 07           | pudding      | 9    | 9       | 18    |
| 08           | gelatin      | 11   | 4       | 15    |
| 09           | potted_meat  | 11   | 7       | 18    |
| 12           | bleach       | 17   | 8       | 25    |
| 15           | drill        | 3    | 4       | 7     |
| 18           | marker       | 3    | 2       | 5     |
|              | **TOTAL**    | 104  | 58      | **162** |

Total size: ~238 MB. Each `.hdf5` is ~1.5 MB.

## Per-episode Schema (HDF5)

| Field              | Shape          | dtype   | Notes |
|--------------------|----------------|---------|-------|
| `state`            | `(31, 8)`      | float32 | `[x,y,z, qw,qx,qy,qz, gripper]` in object-centric G-frame, retarget-quat convention |
| `action`           | `(31, 8)`      | float32 | `state[1:]` (shifted by 1) |
| `point_cloud`      | `(31, 4096, 3)`| float32 | Static CAD surface samples in G-frame (all 31 frames identical; object is static during collection) |
| `obj_origin_G`     | attr `(3,)`    | float64 | Object frame origin in G-frame |
| `obj_quat_G_wxyz`  | attr `(4,)`    | float64 | Object orientation in G-frame |
| `ycb_class_id`     | attr scalar    | int     | DexYCB class id (e.g. 03 = sugar) |

## File Naming

```
dexycb__<session>__<sub-session>__<camera_id>__ycb_dex_NN[_yawDDD].hdf5
```
- No `_yaw` suffix → original DexYCB yaw
- `_yaw90` / `_yaw180` / `_yaw270` → yaw-augmented variant

## Download

```bash
# Option 1: huggingface-cli
huggingface-cli download UCBProject/DP3_DexYCB_training_data \
    --repo-type dataset \
    --local-dir Baseline1/data/episodes_b3_v4_full12_yaw

# Option 2: snapshot_download from Python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="UCBProject/DP3_DexYCB_training_data",
                  repo_type="dataset",
                  local_dir="Baseline1/data/episodes_b3_v4_full12_yaw")
```

The 162 hdf5 files land under `data/` inside this repo, so the
`--local-dir` above ends up with `Baseline1/data/episodes_b3_v4_full12_yaw/data/<162 hdf5>`.
For the UCB_Project pipeline, move them up one level so the path matches the
README:

```bash
mv Baseline1/data/episodes_b3_v4_full12_yaw/data/*.hdf5 \
   Baseline1/data/episodes_b3_v4_full12_yaw/
rmdir Baseline1/data/episodes_b3_v4_full12_yaw/data
```

## Training Pipeline

Full retrain instructions (DexYCB-only, original run):
[`Baseline1/RETRAIN_V4_FULL12.md`](https://github.com/stzabl-png/UCB_Project/blob/gate3-curobo-ik/Baseline1/RETRAIN_V4_FULL12.md)
in the UCB_Project repo.

---

## Combined Training (DexYCB + OakInk → fresh DP3 model)

We are now training a **new DP3 model** that combines this 162-ep DexYCB set
with the 207-ep OakInk set at
[`UCBProject/DP3_OakInk_training_data`](https://huggingface.co/datasets/UCBProject/DP3_OakInk_training_data).

**Important — preserve previous DexYCB-only artefacts**:
- The A6000 already has the previous DexYCB-only DP3 checkpoint
  (`v4_sml` experiment, 3000-epoch run) and the corresponding train/test
  split saved on disk. We still intend to evaluate that model. **The new
  combined run MUST use distinct output paths so nothing is overwritten.**
- **Sim collection** for this round was completed entirely on the dev box
  (RTX 5090). The earlier plan to also run sim collection on A6000 was
  abandoned because the system glibc (2.31) is incompatible with IsaacSim
  5.1's requirement (glibc 2.35). **The A6000 is training-only this round.**
  (See [`UCBProject/baseline_3_v4_collection_assets`](https://huggingface.co/datasets/UCBProject/baseline_3_v4_collection_assets)
  for the deprecated A6000 collection instructions, kept for reference only.)

### Step 1 — Layout the combined dataset in a fresh dir

```bash
cd $HOME/UCB_Project   # the A6000 repo clone

# Fresh dir — do NOT reuse Baseline1/data/episodes_b3_v4_full12_yaw which holds
# the 162-ep DexYCB set and is the training input for the existing model.
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 —
# do NOT re-download).
cp Baseline1/data/episodes_b3_v4_full12_yaw/*.hdf5 "$NEW/"

# 1.2 Download the new OakInk 207 ep
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 a FRESH zarr (do not overwrite the existing one)

```bash
conda activate dp3   # same env A6000 already has

python Baseline1/convert_to_zarr.py \
    "$NEW" \
    --output_zarr Baseline1/data/dp3_train_v4_dexycb162_oakink207.zarr
```

Existing zarr (DexYCB-only) at `Baseline1/data/dp3_train_v4_sml.zarr`
remains untouched.

### Step 3 — Fresh train/test split

The previous split lives in
`third_party/3D-Diffusion-Policy/.../experiments/v4_sml/data_split/`.
**Do not touch it.** Make a new experiment dir:

```bash
cd third_party/3D-Diffusion-Policy/3D-Diffusion-Policy

EXP=dexycb162_oakink207_2026-05-26

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 — Fresh DP3 config + output dir

Copy the prior config and adjust:
```bash
cp config/v4_sml.yaml config/${EXP}.yaml

# Edit config/${EXP}.yaml:
#   task.dataset_zarr_path:   Baseline1/data/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)
#   training.num_epochs:      3000
```

### Step 5 — Launch

```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 — Output lands in a fresh dir

```
experiments/${EXP}/{date_time}/checkpoints/
experiments/${EXP}/{date_time}/wandb/
```

The previous experiment (`experiments/v4_sml/`) and its checkpoint remain
untouched. To re-evaluate the previous model later:
```bash
python eval.py --config-name=v4_sml   # unchanged from before
```

---

## Collection Details

Collected by `sim/run_grasp_sim_baseline3_v4.py` (`gate3-curobo-ik` branch)
with:
- IsaacSim 5.1, PhysX TGS solver, GPU dynamics + CCD
- cuRobo 0.8 motion planner (per-phase mesh toggle for pre-grasp WITH mesh,
  final/lift WITHOUT mesh)
- mass = 0.05 kg (hardcoded; real per-class mass triggers PhysX overflow)
- chunked-5 + retry wrapper to recover from PhysX corruption events

## License

Data: CC-BY-4.0. DexYCB source data subject to the original DexYCB license.

## Citation

If you use this dataset, please also cite DexYCB:
```
@inproceedings{chao2021dexycb,
  title = {DexYCB: A Benchmark for Capturing Hand Grasping of Objects},
  author = {Chao, Yu-Wei and Yang, Wei and Xiang, Yu and Molchanov, Pavlo
            and Handa, Ankur and Tremblay, Jonathan and Narang, Yashraj S
            and Van Wyk, Karl and Iqbal, Umar and Birchfield, Stan and others},
  booktitle = {CVPR},
  year = {2021}
}
```