マルチタスク制御と適応のためのニューラルオペレータ
Neural Operators for Multi-Task Control and Adaptation
タスク記述から最適制御則への写像をニューラルオペレータで学習し、未見タスクや分布外設定への汎化と、軽量更新から全体微調整までの適応戦略を実現した。
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: David Sewell, Xingjian Li, Stepan Tretiakov, Krishna Kumar, David Fridovich-Keil
分類: cs.LG, eess.SY
原文アブストラクト
Neural operator methods have emerged as powerful tools for learning mappings between infinite-dimensional function spaces, yet their potential in optimal control remains largely unexplored. We focus on multi-task control problems, whose solution is a mapping from task description (e.g., cost or dynamics functions) to optimal control law (e.g., feedback policy). We approximate these solution operators using a permutation-invariant neural operator architecture. Across a range of parametric optimal control environments and a locomotion benchmark, a single operator trained via behavioral cloning accurately approximates the solution operator and generalizes to unseen tasks, out-of-distribution settings, and varying amounts of task observations. We further show that the branch-trunk structure of our neural operator architecture enables efficient and flexible adaptation to new tasks. We develop structured adaptation strategies ranging from lightweight updates to full-network fine-tuning, achieving strong performance across different data and compute settings. Finally, we introduce meta-trained operator variants that optimize the initialization for few-shot adaptation. These methods enable rapid task adaptation with limited data and consistently outperform a popular meta-learning baseline. Together, our results demonstrate that neural operators provide a unified and efficient framework for multi-task control and adaptation.
