Humanoid Motion Tracking
Overview
Humanoid motion tracking studies how robots can reproduce human movements and follow motion commands despite differences in body shape, dynamics, and environment. Common approaches retarget motion-capture or video data to robot bodies, then train physics-based controllers with reinforcement learning or imitation; newer systems also use Transformers and generative motion models to broaden the range of skills. Research increasingly tests real-robot transfer, terrain and object interaction, teleoperation, recovery, and adaptation across robot embodiments. A central challenge is balancing faithful, expressive motion with physical feasibility, safety, and robustness, while benchmarks that measure pose error alone may miss unstable contacts or poor task execution.
Latest papers
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