CARAT: Static Compatibility and Dynamic Readiness for Adaptive Human-to-Robot Dexterous Manipulation Transfer

Transferring human demonstrations offers a promising solution to the scarcity of embodiment-specific robot data in dexterous manipulation learning. However, existing human-to-robot transfer methods leave two complementary sources of variation underexplored: the reusability of human interaction priors differs across task-hand pairs, while the learner’s competence varies across trajectory phases and evolves throughout training. To exploit these variations, we propose CARAT, a unified framework that leverages Static Compatibility and Dynamic Readiness to adaptively facilitate human-to-robot manipulation transfer. CARAT formulates a Static Compatibility metric to calibrate task-hand-specific interaction constraints, balancing human prior preservation with alternative contact discovery. It also employs a Dynamic Readiness scheduler to track phase-wise policy competence online, adaptively shifting training emphasis toward the current learning frontier. Across seven manipulation tasks and four heterogeneous dexterous hands, CARAT outperforms baselines under a unified evaluation protocol, demonstrating the vital role of exploiting these variations in human-to-robot transfer.