Work towards MSc degree under the supervision of Prof. Miriam Zacksenhouse
When: 25.1.2022 at 14:00
Abstract: Over the years, industrial robots have been employed increasingly to perform advanced production and high-precision tasks in various industries. However, further integration of industrial robots is hampered by their lack of flexibility, adaptability, and decision-making capabilities compared to human operators. Contact-rich assembly tasks are incredibly challenging since even small uncertainties in the location of the parts can cause large reaction forces and prevent the robot from performing the task successfully. Large industries overcome this problem by designing precise assembly lines. However, this approach is very costly and not economical for small and medium industries where production volumes are not very large. An alternative approach is to use admittance control to handle location uncertainties by endowing the robot’s end-effector with proper stiffness, damping, and inertia properties to correct its position in response to the reaction forces. The power of admittance control for performing assembly tasks motivated a number of researchers to develop machine learning tools to tune the admittance parameters explicitly. However, learning was limited to diagonal admittance matrices and thus required learning the trajectory too. My thesis is based on the understanding that non-diagonal elements in the admittance matrices are critical for correcting the robot’s motion during assembly tasks. In particular, those elements can cause the robot to perform proper translation movements in response to reaction torques due to initial misalignments. In my Thesis, I used Reinforcement learning, and in particular, proximal policy optimization (PPO), to learn the parameters of non-diagonal admittance matrices for peg-in-hole assembly tasks. Learning was performed in simulations and tested in both simulations and on a real robot (UR5e). Results demonstrate that the learned admittance control is robust to uncertainties and can generalize to different locations and sizes. Interestingly, I show that the learned admittance matrices are space invariant. Most importantly, the learned policy was demonstrated successfully on a physical robot, for both rigid pegs and semi-rigid wires, without any retraining. 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.
You can see the seminar here