深層強化学習によるイチゴ自動収穫のための視点最適化
Viewpoint Optimization for Autonomous Strawberry Harvesting with Deep Reinforcement Learning
深層強化学習を用いて、イチゴ収穫ロボットのカメラを好ましい視点へ導く制御方策を学習し、シミュレーション環境でランダム行動より8.7倍高い報酬と視覚サーボ法より8.8%速い探索を達成した。
著者: Jonathon Sather, Xiaozheng Jane Zhang
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Autonomous harvesting may provide a viable solution to mounting labor pressures in the United States's strawberry industry. However, due to bottlenecks in machine perception and economic viability, a profitable and commercially adopted strawberry harvesting system remains elusive. In this research, we explore the feasibility of using deep reinforcement learning to overcome these bottlenecks and develop a practical algorithm to address the sub-objective of viewpoint optimization, or the development of a control policy to direct a camera to favorable vantage points for autonomous harvesting. We evaluate the algorithm's performance in a custom, open-source simulated environment and observe encouraging results. Our trained agent yields 8.7 times higher returns than random actions and 8.8 percent faster exploration than our best baseline policy, which uses visual servoing. Visual investigation shows the agent is able to fixate on favorable viewpoints, despite having no explicit means to propagate information through time. Overall, we conclude that deep reinforcement learning is a promising area of research to advance the state of the art in autonomous strawberry harvesting.