群衆-ロボット相互作用：注意機構付き深層強化学習による群衆認識型ロボットナビゲーション
Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
自己注意機構で人-ロボット間の相互作用を再設計し、人-人間の相互作用も同時にモデル化することで、混雑環境でのロボットの先読みナビゲーション性能を向上させた研究。
著者: Changan Chen, Yuejiang Liu, Sven Kreiss, Alexandre Alahi
分類: cs.RO, cs.LG
原文アブストラクト
Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, their cooperation ability deteriorates as the crowd grows since they typically relax the problem as a one-way Human-Robot interaction problem. In this work, we want to go beyond first-order Human-Robot interaction and more explicitly model Crowd-Robot Interaction (CRI). We propose to (i) rethink pairwise interactions with a self-attention mechanism, and (ii) jointly model Human-Robot as well as Human-Human interactions in the deep reinforcement learning framework. Our model captures the Human-Human interactions occurring in dense crowds that indirectly affects the robot's anticipation capability. Our proposed attentive pooling mechanism learns the collective importance of neighboring humans with respect to their future states. Various experiments demonstrate that our model can anticipate human dynamics and navigate in crowds with time efficiency, outperforming state-of-the-art methods.
