Adaptation to Environmental Change using Reinforcement Learning for Robotic Salamander

Abstract

In the paper, a reinforcement learning technique is applied to produce a central pattern generation-based rhythmic motion control of a robotic salamander while moving toward a fixed target. Since its action spaces are continuous and there are various uncertainties in an environment that the robot moves, it is difficult for the robot to apply a conventional reinforcement learning algorithm. In order to overcome this issue, a deep deterministic policy gradient among the deep reinforcement learning algorithms is adopted. The robotic salamander and the environments where it moves are realized using the Gazebo dynamic simulator under the robot operating system environment. The algorithm is applied to the robotic simulation for the continuous motions in two different environments, i.e., from a firm ground to a mud. Through the simulation results, it is verified that the robotic salamander can smoothly move toward a desired target by adapting to the environmental change from the firm ground to the mud. The gradual improvement in the stability of learning algorithm is also confirmed through the simulations.