Locomotion Planning with 3D Character Animations by Combining Reinforcement Learning Based and Fuzzy Motion Planners

Abstract

Motion and locomotion planning have a wide area of usage in different fields. Locomotion planning with premade character animations has been highly noticed in recent years. Reinforcement Learning presents promising ways to create motion planners using premade character animations. Although RL-based motion planners offer great ways to control character animations but they have some problems that make them hard to be used in practice, including high dimensionality and environment dependency. In this paper we present a motion planner which can fulfill its motion tasks by selecting its best animation sequences in different environments without any previous knowledge of the environment. We combined reinforcement learning with a fuzzy motion planer to fulfill motion tasks. The fuzzy control system commands the agent to seek the goal in environment and avoid obstacles and based on these commands, the agent select its best animation sequences. The motion planner is taught through a reinforcement learning process to find optimal policy for selecting its best animation sequences. To validate our motion planner’s performance, we implemented our method and compared it with a pure RL-based motion planner.