Sensorimotor Alignment: A Training-Free Predictor of Robot Policy Success

Robotic foundation models are typically evaluated through costly closed-loop rollouts, which motivates cheap offline proxies for model capability. The Platonic Representation Hypothesis (PRH) suggests that as representations improve, different modalities such as vision and language converge toward a shared statistical model of reality. We ask whether motion trajectories form another such modality: as robot models become more capable, should their sensory representations converge toward the structure of physical action? To examine this, we propose **Sensorimotor Alignment** (${\mathrm{S}}^2$), a training-free offline metric that measures how well a model’s sensory representation aligns with the geometry of physical action by comparing sensory and trajectory similarity structures. Crucially, ${\mathrm{S}}^2$ consistently and strongly correlates with downstream robot task success across embodied model families, making it a low-cost evaluation proxy. It can also identify promising VLM backbones even before costly VLA fine-tuning. Moreover, we show that ${\mathrm{S}}^2$, as a diagnostic lens, can explain how design choices shape where control-relevant structure is represented in the models. More broadly, ${\mathrm{S}}^2$ offers to bring PRH into embodiment, turning sensorimotor convergence into a measurable object of study.