Kinesthetic Teaching Using Assisted Gravity Compensation for Model-Free Trajectory Generation in Confined Spaces

The presented work approaches programming of redundant robots such as the KUKA Lightweight Robot IV in a co-worker scenario from a user-centered point of view. It specifically asks, how the user’s implicit knowledge about the scene and the task can be transferred effectively to the robot through kinesthetic teaching. It proposes a new method to visualize the implicit scene model conveyed by the user when teaching a respective inverse kinematics and measures generalization by the robot. Based on these insights and empirical results from a previously performed user study, the present study argues that physical guidance of a task in confined spaces with static obstacles is too difficult to achieve in a single interaction. Summarizing earlier results and putting them into context, it is shown how to assist users to remedy this issue. The key is to divide the process in an explicit

configuration phase

for teaching the implicit scene model and a subsequent already assisted

programming phase

to teach the task based on a particular

assisted gravity compensation

mode. Further results from the user study confirm that this renders kinesthetic teaching in confined spaces feasible and enables a flexible and fast reconfiguration of the robot.