Traj2Action: A Co-Denoising Framework for Trajectory-Guided Human-to-Robot Skill Transfer

ArXi:2510.00491v2 Announce Type: replace-cross Learning diverse manipulation skills for real-world robots is severely bottlenecked by the reliance on costly and hard-to-scale teleoperated nstrations. While human videos offer a scalable alternative, effectively transferring manipulation knowledge is fundamentally hindered by the significant morphological gap between human and robotic embodiments. To address this challenge and facilitate skill transfer from human to robot, we