押し動かせる物体環境における強化学習ベースの局所経路計画
Local Path Planning among Pushable Objects based on Reinforcement Learning
物理シミュレーションでA2Cと深層ニューラルネットを用いて複数エージェントを同時訓練し、押し動かせる障害物を軸に沿わない方向にも押して狭い場所での局所経路計画を実現、実機の四足歩行ロボットにも適用した。
著者: Linghong Yao, Valerio Modugno, Andromachi Maria Delfaki, Yuanchang Liu, Danail Stoyanov, Dimitrios Kanoulas
分類: cs.RO
原文アブストラクト
In this paper, we introduce a method to deal with the problem of robot local path planning among pushable objects -- an open problem in robotics. In particular, we achieve that by training multiple agents simultaneously in a physics-based simulation environment, utilizing an Advantage Actor-Critic algorithm coupled with a deep neural network. The developed online policy enables these agents to push obstacles in ways that are not limited to axial alignments, adapt to unforeseen changes in obstacle dynamics instantaneously, and effectively tackle local path planning in confined areas. We tested the method in various simulated environments to prove the adaptation effectiveness to various unseen scenarios in unfamiliar settings. Moreover, we have successfully applied this policy on an actual quadruped robot, confirming its capability to handle the unpredictability and noise associated with real-world sensors and the inherent uncertainties present in unexplored object pushing tasks.
