论文

learning

Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success

The paper proposes Sensorimotor Alignment (S²), a training-free offline metric that compares a model’s sensory representations with the geometry of physical motion, represented by end-effector trajectories. Across 12 vision-language-action models, S² positively correlates with downstream robot task success. The authors present it as a low-cost way to prioritize models and identify promising vision-language backbones before fine-tuning or costly closed-loop evaluation.

翻译待处理 · 英文摘要

arXivOct 22, 2026

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learning

Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success

The paper introduces Sensorimotor Alignment (S²), a training-free offline metric that measures how closely an embodied model’s sensory representations align with the geometry of physical motion. The authors report that S² correlates positively with downstream robot task success across embodied model families and can help prioritize vision-language model backbones before costly fine-tuning into vision-language-action models. It is proposed as a proxy, not a replacement for closed-loop evaluation.

翻译待处理 · 英文摘要

arXiv cs.ROOct 22, 2026

learning

SCOPE: Scene-graph Construction Optimized by Plan Error-correction for Robot Long-term Planning Datasets

SCOPE is an automated framework for generating diverse, executable task-and-motion-planning datasets for robots. It uses planner execution failures to correct logical inconsistencies in scene graphs, aiming to ensure plans are physically feasible. In experiments, data generated by SCOPE improved a fine-tuned downstream TAMP model’s success rate by an absolute 12.33% over a counterpart trained on DELTA-generated data.

翻译待处理 · 英文摘要

arXiv cs.ROOct 22, 2026

learning

Learning Robot Behavior Priors from Non-Robotic Synthetic Data

The paper tests whether a Transformer can learn useful robot-action priors from procedurally generated data based on mathematical abstractions of control, without seeing real robot behavior during pretraining. The model achieves non-trivial zero-shot action prediction on unseen physical robot tasks through in-context learning; specialized control priors outperform generic causal priors. A compute-matched model trained on real robot data performs better only on the datasets it saw during training.

翻译待处理 · 英文摘要

arXiv cs.ROOct 22, 2026

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