Skill Imitation Learning on Dual-arm Robotic Systems

Abstract: A control system used to control two Panda Franka Emika robots online and simultaneously with two HTC Vive controllers is presented, with the primary purpose of demonstrating tasks for robots. The system is validated by learning from demonstration/imitation learning task via Principle Component Analysis (PCA). The task consists of learning different bimanual movement patterns e.g. for drawing sketches, with latent variables that then can be manipulated by the user to generate new shapes of similar structure. Tasks of various correlations between the arms are tested and compared. The system uses components and adaptations e.g. preexisting modules for sensing, communication, motion planning, etc. to realize the goal of modularity and support for other robots than the one used in this thesis. The most prominent systems used are the Robot Operating System (ROS) for the base framework for handling packages and sending information between different parts of the system, and MoveIt’s planning library (running on ROS) for managing kinematics and collision.