表形式基盤モデルはロボット方策学習の探索を導けるか？
Can Tabular Foundation Models Guide Exploration in Robot Policy Learning?
高次元連続制御における方策最適化の探索を改善するため、局所更新と表形式基盤モデルによる大域的探索を組み合わせたハイブリッド手法TFM-S3を提案し、限られたロールアウト予算で収束を加速し最終性能を向上させることを示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Buqing Ou, Frederike Dümbgen
分類: cs.RO, cs.LG
原文アブストラクト
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 more 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. We interleave high-frequency local updates with intermittent rounds of global search. In each search round, we construct a dynamically updated low-dimensional policy subspace via SVD and perform iterative surrogate-guided refinement within this space. A pretrained tabular foundation model predicts candidate returns from a small context set, enabling large-scale screening with limited rollout cost. Experiments on continuous control benchmarks show that TFM-S3 consistently accelerates early-stage convergence and improves final performance compared to TD3 and population-based baselines under an identical rollout budget. These results demonstrate that foundation models are a powerful new tool for creating sample-efficient policy learning methods for continuous control in robotics.
