You can predict whether a robot model will succeed at tasks by checking if its visual representations align with motion geometry, without running expensive robot experiments—this cheap offline check helps prioritize which models to test in the real world.
This paper proposes Sensorimotor Alignment (S²), a training-free metric that measures how well a robot model's visual representations align with the geometry of physical motion.