AI RESEARCH
TrajBooster: Boosting Humanoid Whole-Body Manipulation via Trajectory-Centric Learning
arXiv CS.CV
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ArXi:2509.11839v3 Announce Type: replace-cross Recent Vision-Language-Action models show potential to generalize across embodiments but struggle to quickly align with a new robot's action space when high-quality nstrations are scarce, especially for bipedal humanoids. We present TrajBooster, a cross-embodiment framework that leverages abundant wheeled-humanoid data to boost bipedal VLA. Our key idea is to use end-effector trajectories as a morphology-agnostic interface.