robotics
The Robot Model Can Be Ranked Before It Moves
A training-free score compares sensory representations with physical-motion geometry and correlates with downstream policy success.
Summary
A training-free score compares sensory representations with physical-motion geometry and correlates with downstream policy success.
Closed-loop robot rollouts are expensive, so model builders need cheaper ways to decide which backbones deserve them. Sensorimotor Alignment measures whether a model's sensory representation has the same relational geometry as physical motion trajectories. The authors report positive correlations with task success across embodied model families and use the score to identify promising vision-language backbones before vision-language-action fine-tuning. The metric also offers representation-level interpretations of several embodied-model behaviors. It is an offline prioritization proxy under the studied families, not a substitute for safety testing or real-world evaluation.
Why it matters
A training-free score compares sensory representations with physical-motion geometry and correlates with downstream policy success.
Limits and context
- It is an offline prioritization proxy under the studied families, not a substitute for safety testing or real-world evaluation.
Key claims
A training-free score compares sensory representations with physical-motion geometry and correlates with downstream policy success.
Qualification: It is an offline prioritization proxy under the studied families, not a substitute for safety testing or real-world evaluation.
Evidence: source-2026-10-23-007
Sources
- arXiv preprint 2610.31670, original v1arXiv · primary research
Corrections
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