Relationship between the Order for Motor Skill Transfer and Motion Complexity in Reinforcement Learning

Abstract

We propose a method to generate an order for learning and transferring motor skills based on motion complexity, then evaluate the order to learn motor skills of a task and transfer them to another task as a form of reinforcement learning (RL). Here, motion complexity refers to the complexity calculated from multiple motion trajectories of a task. To do this, multiple human demonstrations are extracted and clustered to calculate motion complexity and identify the motor skills involved in a task. The motion trajectories of the task are then used to calculate the motion complexity considering temporal entropy and spatial entropy. Finally, both orders [Simple-to-Complex] and [Complex-to-Simple] are generated to learn and transfer motor skills based on the motion complexities of multiple tasks. To evaluate these orders, two tasks [Drawing] and [Fitting] are performed using an actual robotic arm. To verify the learning and transfer processes, we apply our method to three different figures as well as to pegs and holes of three different shapes and analyze the experimental results. In addition, we provide guidelines for using the [Simple-to-Complex] and [Complex-to-Simple] orders in RL.