歩行者環境におけるマルチロボットの社会性を考慮した協調計画：マルチエージェント強化学習によるアプローチ
Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning
歩行者がいる環境で複数のロボットが安全かつ効率的に協調移動できるよう、時空間グラフとKステップ先読み報酬を用いたマルチエージェント強化学習手法を提案した。
著者: Zichen He, Chunwei Song, Lu Dong
分類: cs.RO, cs.AI
原文アブストラクト
Safe and efficient co-planning of multiple robots in pedestrian participation environments is promising for applications. In this work, a novel multi-robot social-aware efficient cooperative planner that on the basis of off-policy multi-agent reinforcement learning (MARL) under partial dimension-varying observation and imperfect perception conditions is proposed. We adopt temporal-spatial graph (TSG)-based social encoder to better extract the importance of social relation between each robot and the pedestrians in its field of view (FOV). Also, we introduce K-step lookahead reward setting in multi-robot RL framework to avoid aggressive, intrusive, short-sighted, and unnatural motion decisions generated by robots. Moreover, we improve the traditional centralized critic network with multi-head global attention module to better aggregates local observation information among different robots to guide the process of individual policy update. Finally, multi-group experimental results verify the effectiveness of the proposed cooperative motion planner.
