Acquisition of Four-legged Running Robot Movement Policy Based on Reinforcement Learning to Move up a Slope

Abstract

Nowadays, autonomous robots are being developed for a variety of situations. However, it is difficult for many of these robots to adapt to environmental changes and unexpected situations. One approach to solving this problem is reinforcement learning. By applying this learning to robot control, a robot can learn and acquire the optimal behavior to achieve a task in a given environment. In this study, we applied reinforcement learning to the task of reaching an upslope goal using our robot modeled on four-legged animals. In simulation experiments, when the robot learned under the condition that it was able to determine the angle and speed of leg-swinging motion as actions, it reached a goal on a five-degree upslope. When the robot learned under the condition that it was able to determine the duty cycle of supporting-leg motion simultaneously with other action variables, it reached a goal on a seven-degree upslope.