Using Knowledge Representation and Task Planning for Robot-agnostic Skills on the Example of Contact-Rich Wiping Tasks

TL;DR

This paper presents a flexible robot skill framework that uses knowledge representation and task planning to enable contact-rich wiping tasks across different robot systems and control interfaces.

Contribution

It introduces a skill-based control platform that automatically selects implementations based on input parameters, addressing heterogeneity in robot kinematics and control interfaces.

Findings

Successful execution of contact-rich wiping tasks on different robot systems.

Demonstrated adaptability to various kinematics, grippers, and control interfaces.

Enhanced flexibility in robot programming for Industry 4.0 applications.

Abstract

The transition to agile manufacturing, Industry 4.0, and high-mix-low-volume tasks require robot programming solutions that are flexible. However, most deployed robot solutions are still statically programmed and use stiff position control, which limit their usefulness. In this paper, we show how a single robot skill that utilizes knowledge representation, task planning, and automatic selection of skill implementations based on the input parameters can be executed in different contexts. We demonstrate how the skill-based control platform enables this with contact-rich wiping tasks on different robot systems. To achieve that in this case study, our approach needs to address different kinematics, gripper types, vendors, and fundamentally different control interfaces. We conducted the experiments with a mobile platform that has a Universal Robots UR5e 6 degree-of-freedom robot arm with position control and a 7 degree-of-freedom KUKA iiwa with torque control.