Iteratively Learned and Temporally Scaled Force Control with application to robotic assembly in unstructured environments

Robotic assembly tasks are subject to uncertainties arising from part tolerances. A popular approach to deal with such partially unstructured environments is to introduce compliance by using admittance control schemes. For such schemes, contact forces and torques are speed dependent, which often limits the achievable assembly speed. To overcome this limitation, we present a new method for increasing the achievable speed of compliant manipulators by iteratively reducing contact forces. The presented concept of Iteratively Learned and Temporally Scaled Force Control (ILTSFC) is based on two coupled iterative learning controllers where one increases the assembly speed and the other one adjusts the reference trajectory to reduce contact forces. The approach is verified by an experimental peg-in-hole study on an ABB YuMi, a dual-arm collaborative robot with 7DOF each arm.