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A subset of the MolmoAct2-BimanualYAM dataset, a large-scale collection of bimanual robot manipulation demonstrations created using LeRobot. The full collection contains more than 720 hours of training demonstrations for diverse tabletop manipulation tasks. This subset includes annotated language instructions and was published by AllenAI in 2026.

Use Cases

• Training imitation learning models based on recorded robot demonstration trajectories.
• Developing language-conditioned robot policies based on the annotated language instructions.
• Benchmarking bimanual manipulation algorithms on diverse tabletop tasks.
• Studying the relationship between natural language commands and multi-arm robot actions.

Strengths

• Contains more than 720 hours of training demonstrations in the full collection, indicating a substantial scale.
• Focuses on bimanual manipulation, a complex and less common robotics domain.
• Includes annotated language instructions, which can support language-to-action research.

Limitations

• Description metadata is limited; actual data quality, structure, and the specific content of this subset require manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.

Provenance

Source

AllenAI, created using LeRobot.

Collection Method

Collection of robot manipulation demonstrations.

Freshness

Last updated 2026-05-05 14:44:20.