M.Sc Thesis


M.Sc StudentKozlovsky Shir
SubjectLearning Admittance Control for Contact-Rich Assembley
Skills
DepartmentDepartment of Autonomous Systems and Robotics
Supervisor PROF. Miriam Zacksenhouse
Full Thesis textFull thesis text - English Version


Abstract

Robotic manipulators are playing an increasing role in a wide range of industries. However, their application to assembly tasks is hampered by the need for precise control over the environment and for task-specific coding. Impedance control is a well-established method for interacting with the environment and handling uncertainties. With the advance of Reinforcement Learning (RL), it has been suggested to learn the impedance matrices. However, most of the current work is limited to learning diagonal impedance matrices in addition to the trajectory itself. We argue that asymmetric impedance matrices enhance the ability to properly correct reference trajectories generated by a baseline planner, alleviating the need for learning the trajectory. Moreover, a task-specific set of asymmetric impedance matrices can be sufficient for simple tasks, alleviating the need for learning variable impedance control. We learn impedance policies for small (few mm) peg-in-hole using model-free RL and investigate the advantage of using asymmetric impedance matrices and their space-invariance. Finally, we demonstrate that the policy learned in simulation can be transferred to a real robot without retraining and that it generalizes well to different sizes and to semi-flexible pegs.  This research is funded by Israel Innovation Authorities (IIA) as part of the ART (Assembly by Robotic Technology) academia-industry cooperation (” MAGNET”) aimed to develop generic tools for increasing robotic integration in the industry, especially for small to medium volumes.