yuuu94/W2-VLA-CoT

World-to-Wrist: Offline CoT Labels

This dataset contains frame-aligned offline chain-of-thought annotations used to train W²-VLA policies on LIBERO, RoboTwin, and four real-world manipulation tasks. Matching LeRobot action data is available in W2-VLA-Training-Data.

Dataset Structure

W2-VLA-CoT/
├── libero/
│   ├── libero_10_no_noops_1.0.0_lerobot/
│   ├── libero_goal_no_noops_1.0.0_lerobot/
│   ├── libero_object_no_noops_1.0.0_lerobot/
│   └── libero_spatial_no_noops_1.0.0_lerobot/
├── robotwin/
│   └── <50 task directories>/
├── real_world/
│   ├── place_bag/
│   ├── put_mango/
│   ├── table_clean/
│   └── plug_in_socket/
└── dataset_manifest.json

Split	Episodes	Frames
LIBERO	1,693	273,465
RoboTwin	2,500	549,787
Real world: place bag	80	24,000
Real world: put mango	100	31,000
Real world: table clean	100	89,469
Real world: plug in socket	100	75,679
Total	4,573	1,043,400

Annotation Format

Each episode_XXXXXX.npz file contains frame-aligned arrays. The policy is trained with cot_train_text, which has the following three-field format:

Subtask: ...
Reasoning: ...
Wrist: ...

The public schema contains:

schema_version
episode_index
num_frames
task
task_description
task_name
cot_train_text
cot_subtask
cot_reasoning
cot_wrist_focus

Task fields are present when available. The four CoT arrays have length num_frames.

Download

hf download yuuu94/W2-VLA-CoT \
  --repo-type dataset \
  --local-dir playground/Datasets/W2-VLA-CoT

Use libero/, robotwin/, or real_world/ as the label root for the matching training configuration.