Enabling Maintainablity of Robot Programs in Assembly by Extracting Compositions of Force- and Position-Based Robot Skills from Learning-from-Demonstration Models

To this day, only a small number of industrial robots is used in assembly. One key reason for this is that specific contact situations require the introduction of force-control schemes. The parameters for those schemes are hard to select in practice, because they require in-depth expertise about the robot and the process. Learning-from-Demonstration (LfD) provides a powerful approach to intuitively parameterize robot programs by demonstrating the task at hand. However, when dimensions increase by including force or orientation, many LfD algorithms are hard to verify, understand and maintain, requiring expert knowledge to make adaptions, effectively making it a "black-box". This property renders them ineffective for usage in industrial applications. We build upon a system of composable skills, that can be easily adapted by experts without the need to demonstrate the task again. This approach to skill-based robot programming promises to address the issues of readability and maintainability by sequencing robot movements in skills and breaking them down into understandable (sub-)goals. In this paper, we combine skill-based programming with LfD, preserving both maintainability and intuitive parameterization. We present (a) an approach to parameterize and create sequences of hierarchies of force- and/or position-controlled robot skills from a LfD model, (b) which can be adapted by a user by hand with few, basic and understandable parameters, and (c) show its applicability on the real-world example of terminal clamp assembly. We achieve a reduction in teach-in time of 53.8% for variants, increased robustness against variance, and efficient tight stacking of clamps with a gap of ≤ 1mm.