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InterMimicGen: Scaling Humanoid Loco-Manipulation through Self-Evolving Motion Imitation

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Briefings

InterMimicGen consolidates sparse human-object interaction data into a diverse humanoid reference collection by retargeting interactions while preserving whole-body coordination. It trains a physics-based generalist tracker to execute these references in simulation, creating a data flywheel where small, task-preserving edits to demonstrations compound into broader coverage. Experiments confirm contact-preserving retargeting across robot configurations, single generalist policy execution, and successful transfer to real robots.

Key points

• InterMimicGen consolidates sparse human-object interaction datasets into a large, diverse humanoid robot reference collection by retargeting interactions while preserving whole-body coordination and dexterous hand-object relationships.
• A physics-based generalist tracker executes these references in simulation, covering a scale and diversity beyond prior humanoid tracking systems for loco-manipulation.

Context

Humanoid loco-manipulation requires rich supervision from human-object interactions, but existing data is sparse, heterogeneous, and not directly executable by robots. InterMimicGen addresses this by creating a unified path from heterogeneous human demonstrations to a continually expanding motion resource for humanoid robot learning.

Limitations

Evidence covers only the consolidation, tracking, and simulation phases; real-world deployment results and long-term durability under varying conditions remain unverified.

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