Skill Transfer between Humans and Robots Based on Dynamic Movement Primitives and Sparse Autoencoder

Abstract

With the development of robotic industry, subhuman robots are paid more attention. In order to meet people’s requirements, robots need to grasp human’s behaviors and service human beings. Skill transfer is the core for this procedure. In this thesis, we supply a process to transfer behaviors from humans to robots or from robots to robots. The technical contributions of this procedure include: (1) two approaches to capture human’s behavior trajectories; (2) building model to solve robotic kinematics problems; (3) applying Dynamic Movement Primitives (DMP) to achieve targets of reproducing trajectory; (4) combining DMP with Sparse Autoencoder to increase efficient of procedure. As the result, trajectories are transferred successfully from human to robot and from robot to robot. Meanwhile, performances of inverse kinematics and DMP are proved.