言語モデルをロボティクス向け閉ループ高レベルプランナとして活用する：概要とベンチマーク
Using Language Models as Closed-Loop High-Level Planners for Robotics Applications: A Brief Overview and Benchmarks
LLM/VLMをロボットの高レベルプランナとして閉ループで使う際の制御ホライズンやウォームスタートの影響を実験的に調べ、性能と堅牢性を高める実践的指針を示した。
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？
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著者: Hao Wang, Sathwik Karnik, Bea Lim, Somil Bansal
分類: cs.RO, cs.AI
原文アブストラクト
Large Language Models (LLMs) and Vision Language Models (VLMs) have become popular tools for embodied high-level planning. However, their deployment in black-box settings often leads to unpredictable or costly errors. To harness their capabilities more reliably in robotic systems, we empirically investigate practical strategies for integrating language models as closed-loop planners. Concretely, we study how the control horizon and warm-starting impact the performance of language model-based planners. We design and conduct controlled experiments to extract actionable insights, providing recommendations that can help improve the performance and robustness of language model-based embodied planning. The full implementation and experiments are available on the project website
