反応型タスク・動作計画のためのマルチモーダルMPPIと能動推論
Multi-Modal MPPI and Active Inference for Reactive Task and Motion Planning
能動推論による高レベル行動選択とマルチモーダルMPPI制御を組み合わせ、実行時の不確実性や外乱に適応しながらタスクと動作を同時に計画・制御する手法を提案した。
著者: Yuezhe Zhang, Corrado Pezzato, Elia Trevisan, Chadi Salmi, Carlos Hernández Corbato, Javier Alonso-Mora
分類: cs.RO
原文アブストラクト
Task and Motion Planning (TAMP) has made strides in complex manipulation tasks, yet the execution robustness of the planned solutions remains overlooked. In this work, we propose a method for reactive TAMP to cope with runtime uncertainties and disturbances. We combine an Active Inference planner (AIP) for adaptive high-level action selection and a novel Multi-Modal Model Predictive Path Integral controller (M3P2I) for low-level control. This results in a scheme that simultaneously adapts both high-level actions and low-level motions. The AIP generates alternative symbolic plans, each linked to a cost function for M3P2I. The latter employs a physics simulator for diverse trajectory rollouts, deriving optimal control by weighing the different samples according to their cost. This idea enables blending different robot skills for fluid and reactive plan execution, accommodating plan adjustments at both the high and low levels to cope, for instance, with dynamic obstacles or disturbances that invalidate the current plan. We have tested our approach in simulations and real-world scenarios.
