From Legs to Wheels: Embodiment-Aware Human Motion Retargeting for Mobile-Base Humanoids

Abstract:Human video offers a scalable source of robot demonstrations, yet most human-to-humanoid retargeting methods assume a legged robot with human-like kinematics. This assumption does not hold for mobile-base humanoids equipped with a wheeled base, vertical lift, and two arms. Human walking must be expressed through base motion, while torso bending may require coordinated lift and arm motion. We address this mismatch with a task-conditioned framework that assigns reconstructed human motion to base, lift, and arm responsibilities before robot-specific realization. The allocator preserves the human-derived path, stabilizes heading, separates turn and translation when needed, retimes commands to satisfy base limits, and repairs lift and arm trajectories. A deployment adapter then converts the reference to 50 Hz commands using stationary-base detection, deadband and slew-rate filtering, time-consistent playback scaling, and separate linear and angular gains. We evaluate the resulting references with human-derived task-space comparisons, policy-free simulation replay, and a qualitative execution on a physical robot.

Robotics (cs.RO)

arXiv:2610.08381 [cs.RO]

(or arXiv:2610.08381v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.08381 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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