速く考え、選択的に計画する：効率的なデータ駆動型MPCのための適応的熟考
Think Fast, Plan Selectively: Adaptive Deliberation for Efficient Data-Driven MPC
人間の二重過程理論に着想を得て、高速なポリシー実行とテスト時計画を適応的に切り替えるFast-TD-MPCを提案し、103の連続制御タスクで最大4倍の推論高速化を達成した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Yi Xian Goh, Sze Jue Yang, Hao Luan
分類: cs.RO, cs.LG
原文アブストラクト
Data-driven model predictive control (MPC) combines learned world models with online trajectory optimization, achieving strong performance in continuous control. However, the per-step cost of sampling and evaluating hundreds of candidate trajectories restricts deployment to control frequencies well below what real-time robotics demands. Motivated by the dual-process theory of human cognition, which distinguishes between fast, intuitive processing (System 1) and slower, deliberative reasoning (System 2), we ask whether every decision requires the same degree of computational deliberation. We propose Fast-TD-MPC, a lightweight framework that adaptively routes between fast policy execution and test-time planning, reserving costly deliberation for states where it is most needed. Fast-TD-MPC delivers competitive task performance across 103 continuous control tasks while achieving up to ~4x faster inference. Under external disturbances, Fast-TD-MPC selectively falls back to planning, maintaining robustness comparable to the original planner.
