Sensorimotor Alignment：衡量具身模型能力的新型离线指标
Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success
这篇论文提了个省钱的办法：不用跑昂贵的机器人闭环测试，用 S² 指标就能提前判断哪个模型训出来更靠谱。做具身智能的值得看看。
论文提出 Sensorimotor Alignment（S²），一种无需训练的离线指标，用于衡量模型感知表征与物理运动几何结构的对齐程度。研究发现 S² 与多个具身模型家族的下游机器人任务成功率呈正相关。在将 VLM 微调为 VLA 之前，S² 可以预先识别有潜力的视觉语言模型骨干，省去昂贵的闭环评估。该工作还把 Platonic Representation Hypothesis 扩展到具身模型，并为 VLA 与世界动作模型（WAM）中的现象提供表征层面的解释。
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 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.