ロボット情報収集のための学習ベースパラメータ選択
Learned Parameter Selection for Robotic Information Gathering
強化学習エージェントが情報経路計画のパラメータを各反復で自動選択する手法を提案し、多様な環境でエンドツーエンド学習より9.53%高い累積報酬を達成した。
著者: Christopher E. Denniston, Gautam Salhotra, Akseli Kangaslahti, David A. Caron, Gaurav S. Sukhatme
分類: cs.RO
原文アブストラクト
When robots are deployed in the field for environmental monitoring they typically execute pre-programmed motions, such as lawnmower paths, instead of adaptive methods, such as informative path planning. One reason for this is that adaptive methods are dependent on parameter choices that are both critical to set correctly and difficult for the non-specialist to choose. Here, we show how to automatically configure a planner for informative path planning by training a reinforcement learning agent to select planner parameters at each iteration of informative path planning. We demonstrate our method with 37 instances of 3 distinct environments, and compare it against pure (end-to-end) reinforcement learning techniques, as well as approaches that do not use a learned model to change the planner parameters. Our method shows a 9.53% mean improvement in the cumulative reward across diverse environments when compared to end-to-end learning based methods; we also demonstrate via a field experiment how it can be readily used to facilitate high performance deployment of an information gathering robot.
