Using Robot Skills for Flexible Reprogramming of Pick Operations in Industrial Scenarios

Abstract

Traditional robots used in manufacturing are very efficient for solving specific tasks that are repeated many times. The robots are, however, difficult to (re-)configure and (re-)program. This can often only be done by expert robotic programmers, computer vision experts, etc., and it requires additionally lots of time. In this paper we present and use a skill based framework for robotic programming. In this framework, we develop a flexible pick skill, that can easily be reprogrammed to solve new specific tasks, even by non-experts. Using the pick skill, a robot can detect rotational symmetric objects on tabletops and pick them up in a user-specified manner. The programming itself is primarily done through kinesthetic teaching. We show that the skill has robustness towards the location and shape of the object to pick, and that objects from a real industrial production line can be handled. Also, preliminary tests indicate that non-expert users can learn to use the skill after only a short introduction.