Local Path Planning among Pushable Objects Based on Reinforcement Learning

In this paper, we introduce a method to tackle the problem of robot local path planning among pushable objects – an open problem in robotics. In particular, we simultaneously train multiple agents in a physics-based simulation environ- ment, 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 in unexplored object-pushing tasks.