Robot Learning

Embodiment Gap

Embodiment Gap is the mismatch in morphology, kinematics, sensing, and dynamics between the agent that generated training data and the robot executing the learned behavior. It arises most sharply when learning from human video, where hands, arm geometry, and viewpoints differ from grippers and robot cameras, and also between distinct robot platforms. Mitigations include action and viewpoint retargeting, embodiment-agnostic representations, and co-training on data from both embodiments.

Why it matters for physical AI

Human video and heterogeneous robot fleets are the largest available data sources for manipulation, and the embodiment gap is precisely what limits how much of that data transfers to any given platform.