Thursday, 29 October 2026 · 6 min read
Sensorimotor Alignment predicts robot task success from offline sensory-motor representation similarity
This paper introduces Sensorimotor Alignment (S²), a training-free metric measuring how well a robot model's sensory representations align with its motor trajectories, showing strong correlation with task success across model families and real robots, outperforming existing offline proxies.
Based on the paper Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success by Yunfei Xie, Howard H. Qian, Shiyi Lan, Kaiyu Hang and 1 other
Challenges in evaluating embodied robot models and the need for offline proxies
Evaluating robot foundation models typically requires costly closed-loop rollouts in simulation or real-world environments, which are slow, noisy, and depend heavily on specific benchmarks.
Existing offline metrics often assess passive perception quality, such as visual question answering (VQA) scores or video generation quality, but do not capture whether sensory representations are organized to support the robot's actions.
The Platonic Representation Hypothesis (PRH) suggests that different modalities (vision, language) converge to shared representations of the world; this paper extends PRH to robotics by hypothesizing that sensory representations should align with motor trajectories as models become more capable.
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