Interpreting Behaviors and Geometric Constraints as Knowledge Graphs for Robot Manipulation Control

TL;DR

This paper explores using knowledge graphs to interpret actions and geometric constraints for robot manipulation, enhancing explainability and flexibility in control systems.

Contribution

It introduces a novel approach that unifies behavior trees and geometric constraints via knowledge graphs for improved robot manipulation control.

Findings

Knowledge graphs enable explainable manipulation control.

The approach supports flexible and natural interaction modeling.

Real-world tests demonstrate effectiveness and interpretability.

Abstract

In this paper, we investigate the feasibility of using knowledge graphs to interpret actions and behaviors for robot manipulation control. Equipped with an uncalibrated visual servoing controller, we propose to use robot knowledge graphs to unify behavior trees and geometric constraints, conceptualizing robot manipulation control as semantic events. The robot knowledge graphs not only preserve the advantages of behavior trees in scripting actions and behaviors, but also offer additional benefits of mapping natural interactions between concepts and events, which enable knowledgeable explanations of the manipulation contexts. Through real-world evaluations, we demonstrate the flexibility of the robot knowledge graphs to support explainable robot manipulation control.