Add combined-training (DexYCB+OakInk) section pointing to new OakInk dataset; preserve previous v4_sml ckpt paths

README.md

@@ -29,6 +29,12 @@ 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 |
@@ -94,10 +100,123 @@ rmdir Baseline1/data/episodes_b3_v4_full12_yaw/data
 
 ## Training Pipeline
 
-Full retrain instructions:
+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)