視覚に基づく追跡・逃避ロボット政策の学習
Learning Vision-based Pursuit-Evasion Robot Policies
完全観測可能な政策で部分観測可能な政策を教師あり学習し、RGB-Dカメラを搭載した四足歩行ロボットで追跡・逃避行動を実現した。
著者: Andrea Bajcsy, Antonio Loquercio, Ashish Kumar, Jitendra Malik
分類: cs.RO, cs.AI
原文アブストラクト
Learning strategic robot behavior -- like that required in pursuit-evasion interactions -- under real-world constraints is extremely challenging. It requires exploiting the dynamics of the interaction, and planning through both physical state and latent intent uncertainty. In this paper, we transform this intractable problem into a supervised learning problem, where a fully-observable robot policy generates supervision for a partially-observable one. We find that the quality of the supervision signal for the partially-observable pursuer policy depends on two key factors: the balance of diversity and optimality of the evader's behavior and the strength of the modeling assumptions in the fully-observable policy. We deploy our policy on a physical quadruped robot with an RGB-D camera on pursuit-evasion interactions in the wild. Despite all the challenges, the sensing constraints bring about creativity: the robot is pushed to gather information when uncertain, predict intent from noisy measurements, and anticipate in order to intercept. Project webpage: https://abajcsy.github.io/vision-based-pursuit/
