Self-Generating Evaluations for Robot’s Autonomy Based on Sensor Input

Abstract

Reinforcement learning has been explored within the context of robot operation in different environments. Designing the reward function in reinforcement learning is challenging for designers because it requires specialized knowledge. To reduce the design burden, we propose a reward design method that is independent of both specific environments and tasks in which reinforcement learning robots evaluate and generate rewards autonomously based on sensor information received from the environment. This method allows the robot to operate autonomously based on sensors. However, the existing approach to adaption attempts to adapt without considering the input properties for the strength of the sensor input, which may cause a robot to learn harmful actions from the environment. In this study, we propose a method for changing the threshold of a sensor input while considering the strength of the input and other properties. We also demonstrate the utility of the proposed method by presenting the results of simulation experiments on a path-finding problem conducted in an environment with sparse rewards.