aiqbench analysis
This paper proposes Sensorimotor Alignment (S²), a training-free offline metric that checks how well a robot model's visual representations match the geometry of physical motion, as a cheaper stand-in for costly closed-loop evaluation. The reported correlation with downstream task success is promising, but it rests on the authors' own model families and benchmarks, so generalization across architectures and tasks needs independent testing. For readers building embodied systems, the practical question is whether S² stays predictive beyond the settings tested.
Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success
arXiv:2610.31670v1 Announce Type: cross Abstract: 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 embodied models become more capable, should their sensory representations converge toward the structure of physical motion? To examine this, we propose Sensorimotor Alignment ($S^2$), a training-free offline metric that measures how well a model's sensory representation aligns with the geometry of physical motion. We find that $S^2$ correlates positively with downstream robot task success across embodied model families, making it a low-cost offline proxy for prioritizing models before closed-loop evaluation. $S^2$ can also identify promising vision-language model (VLM) backbones even before costly fine-tuning into vision-language-action models (VLAs). Beyond this correlation, $S^2$ offers representation-level explanations for phenomena observed in VLAs and world action models (WAMs). With $S^2$, we extend PRH to embodied models and make sensorimotor alignment empirically measurable, offering insight into why an embodied model succeeds beyond its closed-loop success rate.
Abstract: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 embodied models become more capable, should their sensory representations converge toward the structure of physical motion? To examine this, we propose Sensorimotor Alignment ($S^2$), a training-free offline metric that measures how well a model's sensory representation aligns with the geometry of physical motion. We find that $S^2$ correlates positively with downstream robot task success across embodied model families, making it a low-cost offline proxy for prioritizing models before closed-loop evaluation. $S^2$ can also identify promising vision-language model (VLM) backbones even before costly fine-tuning into vision-language-action models (VLAs). Beyond this correlation, $S^2$ offers representation-level explanations for phenomena observed in VLAs and world action models (WAMs). With $S^2$, we extend PRH to embodied models and make sensorimotor alignment empirically measurable, offering insight into why an embodied model succeeds beyond its closed-loop success rate.
28 pages. Project page and code available at this https URL
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
arXiv:2610.31670 [cs.CV]
(or arXiv:2610.31670v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.31670 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 297 words
Related coverage
MotorSense: A Video-EMG Dataset of Motoric Representations for Action Understanding
arXiv · Nov 2, 2026
Self-Supervised Anchoring of Fingertip Sensing to Proprioception and Proactive Actions for Robot Imitation Learning
arXiv · Sep 25, 2026
Multilingual Brain Decoding Reveals Shared Cortical Semantic Geometry
arXiv · Oct 21, 2026
Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly
arXiv · Jun 16, 2026