Optimizing Programming by Demonstration for In-contact Task Models by Incremental Learning

Abstract: Despite the increasing usage of robots for industrial applications, many aspects prevent robots from being used in daily life. One of these aspects is that extensive knowledge in programming a robot is necessary to make the robot achieve a desired task. Conventional robot programming is complex, time consuming and expensive, as every aspect of a task has to be considered. Novel intuitive and easy to use methods to program robots are necessary to facilitate the usage in daily life.
This thesis proposes an approach that allows a novice user to program a robot by demonstration and provides assistance to incrementally refine the trained skill. The user utilizes kinesthetic teaching to provide an initial demonstration to the robot. Based on the information extracted from this demonstration the robot starts executing the demonstrated task. The assistance system allows the user to train the robot during the execution and thus refine the model of the task.
Experiments with a KUKA LWR4+ industrial robot evaluate the performance of the assistance system and advantages over unassisted approaches. Furthermore a user study is performed to evaluate the interaction between a novice user and robot.