AI RESEARCH
Can Tabular Foundation Models Guide Exploration in Robot Policy Learning?
arXiv CS.LG
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ArXi:2604.27667v1 Announce Type: cross Policy optimization in high-dimensional continuous control for robotics remains a challenging problem. Predominant methods are inherently local and often require extensive tuning and carefully chosen initial guesses for good performance, whereas global and less initialization-sensitive search methods typically incur high rollout costs. We propose TFM-S3, a tabular hybrid local-global method for improving global exploration in robot policy learning with limited rollout cost.