Yucheng Mao

What’s missing from robot foundation models? An explicit understanding of WHY motions work: why move in this direction, why place the object in this way, how hard to grasp the object. This motion-level reasoning bridges instructions to actions—and provides knowledge that can generalize across embodiments and scenes. Pure behavior cloning supervises WHAT actions to take, but leaves models to infer WHY those actions work. This becomes especially limiting when learning from large-scale heterogeneous data, including egocentric human data, where humans and robots can take very different actions to achieve the same physical effect. We need models to extract the intent behind those motions, rather than simply recite them. Why leave that knowledge buried in trajectories when we can teach it explicitly? Introducing EgoLAP: a shared language for human and robot motion. Language actions teach HOW to move; motion-level reasoning teaches WHY the motion works. Together, they unlock, to our knowledge, unprecedented gains in zero-shot performance for robot foundation models without additional robot demonstrations. arxiv.org/abs/2610.08726 ego-lap.github.io/