zhouxueyang/LIBERO-Pro2.0-Train

LIBERO-Pro2.0 Training Data

Robot manipulation demonstrations in native HDF5 format for training imitation-learning and vision-language-action policies. The data covers LIBERO-Spatial, LIBERO-Object, LIBERO-Goal, and LIBERO-10.

• 40,470 stored demonstrations across 32 base tasks and 1,349 task/variation instances.
• Language instructions, two 128 × 128 RGB camera views, robot states, and 7-dimensional actions.
• Training data only; no validation or test split.

Preview trajectories · Watch example videos

Download

pip install -U huggingface_hub h5py

hf download zhouxueyang/LIBERO-Pro2.0-Train \
  --repo-type dataset --include "data/**" \
  --local-dir ./LIBERO-Pro2.0-Train

The training files require approximately 220 GiB, plus cache space. For one suite, replace data/** with data/libero_spatial/**.

Read a demonstration

Each episodes.hdf5 file contains 30 demonstrations under data/demo_0 through data/demo_29.

import h5py

path = "LIBERO-Pro2.0-Train/data/libero_spatial/task_1/BASE/episodes.hdf5"
with h5py.File(path, "r") as f:
    demo = f["data/demo_0"]
    actions = demo["actions"][:]                         # (T, 7)
    scene_images = demo["obs/agentview_image"][:]         # (T, 128, 128, 3)
    wrist_images = demo["obs/robot0_eye_in_hand_image"][:] # (T, 128, 128, 3)

Use these sequences with your policy's training loader. See the data format guide for robot-state fields, instruction metadata, and action semantics.

License

CC BY-NC 4.0.