LeWAM/lewam-toolhang

LeWAM Tool Hang

Simulation training data used by LeWAM.

• Episodes: 200
• Frames: 95,962
• Camera columns: pixels
• Action dimensions: 7

Files

File	Bytes	Purpose
toolhang.h5	5,030,456,346	Training data

Download and read

pip install huggingface_hub h5py hdf5plugin

from pathlib import Path
from huggingface_hub import snapshot_download
import hdf5plugin  # registers the image compression filters
import h5py

root = Path(snapshot_download("LeWAM/lewam-toolhang", repo_type="dataset"))
with h5py.File(root / "toolhang.h5", "r") as data:
    print(list(data.keys()))
    image = data["pixels"][0]
    action = data["action"][0]

Integrity and format

manifest.json records the byte size and SHA-256 of every HDF5 file. Camera frames are 224 × 224 RGB. ep_offset and ep_len describe episode boundaries. No optional preload cache is required.

The companion data files preserve the source training data bytes. Multiview entry points only combine columns; no trajectories or camera views are synthesized.