言語モデルによるメタ最適化とプログラム探索を用いたタスク・動作計画
Meta-Optimization and Program Search using Language Models for Task and Motion Planning
基盤モデルとロボット制御のインターフェースとして軌道最適化問題のプログラム探索を行い、ゼロ次最適化で数値パラメータを調整するTAMP手法を提案し、物体操作と描画タスクで有効性を示した。
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？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Denis Shcherba, Eckart Cobo-Briesewitz, Cornelius V. Braun, Marc Toussaint
分類: cs.RO
原文アブストラクト
Intelligent interaction with the real world requires robotic agents to jointly reason over high-level plans and low-level controls. Task and motion planning (TAMP) addresses this by combining symbolic planning and continuous trajectory generation. Recently, foundation model approaches to TAMP have presented impressive results, including fast planning times and the execution of natural language instructions. Yet, the optimal interface between high-level planning and low-level motion generation remains an open question: prior approaches are limited by either too much abstraction (e.g., chaining simplified skill primitives) or a lack thereof (e.g., direct joint angle prediction). Our method introduces a novel technique employing a form of meta-optimization to address these issues by: (i) using program search over trajectory optimization problems as an interface between a foundation model and robot control, and (ii) leveraging a zero-order method to optimize numerical parameters in the foundation model output. Results on challenging object manipulation and drawing tasks confirm that our proposed method improves over prior TAMP approaches.
