Sensorimotor Alignment: A Platonic Proxy for Embodied 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 an
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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 an
This paper introduces a novel, cost-effective offline metric for evaluating embodied AI models, which is crucial for accelerating research and development in robotics. By proposing Sensorimotor Alignment, it addresses the significant challenge of expensive closed-loop rollouts, making model iteration and improvement more efficient.