CLARU
gate-institute2025

GATE-VLAP Datasets

Preprocessed datasets from the LIBERO benchmark suite in WebDataset TAR format, specifically designed for training vision-language-action models with semantic action segmentation.

Downloads495
Episodes2229
Likes3

Why This Matters for Physical AI

This dataset enables training of vision-language-action models with semantic action segmentation, advancing the development of generalist robotic agents that can understand and execute complex, long-horizon manipulation tasks.

Technical Profile

Modalities
rgblanguage
Action Space
end_effector_delta
Environment
simulation
Task Types
manipulationpick_and_placegrasping
Episodes
2229
Data Format
WebDataset
Annotation Types
language_instructionsaction_labelssemantic_action_segmentation
Part of the LIBERO family

Community Signals

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