おいロボット！大規模言語モデルとモデル予測制御によるロボットナビゲーションの個人化
Hey Robot! Personalizing Robot Navigation through Model Predictive Control with a Large Language Model
ユーザーの自然言語指示や環境画像を視覚言語モデルで解釈し、モデル予測制御のコスト関数とパラメータを再構成することで、ロボットのナビゲーション動作をゼロショットで個人化する手法を提案した。
著者: Diego Martinez-Baselga, Oscar de Groot, Luzia Knoedler, Javier Alonso-Mora, Luis Riazuelo, Luis Montano
分類: cs.RO
原文アブストラクト
Robot navigation methods allow mobile robots to operate in applications such as warehouses or hospitals. While the environment in which the robot operates imposes requirements on its navigation behavior, most existing methods do not allow the end-user to configure the robot's behavior and priorities, possibly leading to undesirable behavior (e.g., fast driving in a hospital). We propose a novel approach to adapt robot motion behavior based on natural language instructions provided by the end-user. Our zero-shot method uses an existing Visual Language Model to interpret a user text query or an image of the environment. This information is used to generate the cost function and reconfigure the parameters of a Model Predictive Controller, translating the user's instruction to the robot's motion behavior. This allows our method to safely and effectively navigate in dynamic and challenging environments. We extensively evaluate our method's individual components and demonstrate the effectiveness of our method on a ground robot in simulation and real-world experiments, and across a variety of environments and user specifications.
