Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success

The paper introduces Sensorimotor Alignment (S²), a training‑free offline metric that evaluates how well a model’s sensory representations match the geometry of physical motion. It demonstrates that higher S² scores correlate with better downstream robot task performance across various embodied model families, providing a low‑cost proxy for model selection before expensive closed‑loop testing. Additionally, S² can identify promising vision‑language model backbones early and offers representation‑level explanations for observed behaviors in vision‑language‑action models and world action models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• llms
• robotics
• fine-tuning
• multimodal
• safety