InterMimicGen: Self-Evolving Motion Imitation Scales Humanoid Loco-Manipulation

Yucheng Zhang · hf · 2026-10-06

InterMimicGen: Scaling Humanoid Loco-Manipulation via Self-Evolving Imitation

Captured human-object interactions are rich supervision for humanoid loco-manipulation, but sparse, heterogeneous, and not directly executable. InterMimicGen is a self-evolving motion-imitation framework where robot motion data and a tracking policy improve each other:

• Retargeting: consolidates motion-captured human-object datasets into humanoid robot references, preserving whole-body coordination and dexterous hand-object relationships;
• Generalist tracker: a physics-based tracker executes these references in simulation on a humanoid with dexterous hands, exceeding prior systems in scale and diversity;
• Data flywheel: each round makes small task-preserving changes to where and how an interaction happens, fine-tunes the tracker, keeps only variants that complete the task in simulation, seeding the next round—small edits compound into broader coverage around sparse originals while preserving semantics and motion quality.

Experiments show contact-preserving retargeting across robot configurations, broad tracking with a single generalist policy, growing executable motions over augmentation rounds, and real-robot transfer.

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