Generative Grasp Detection and Estimation with Concept Learning-based Safety Criteria

TL;DR

This paper presents a transparent, safety-aware grasp detection method for collaborative robots, integrating explainable AI to improve tool handling and handover safety in industrial settings.

Contribution

It introduces a novel pipeline combining grasp detection with concept learning-based safety criteria for enhanced transparency and safety in robot tool handling.

Findings

Effective tool detection and grasping in industrial environments

Improved safety and reliability through explainable AI integration

Enhanced handover position accuracy

Abstract

Neural networks are often regarded as universal equations that can estimate any function. This flexibility, however, comes with the drawback of high complexity, rendering these networks into black box models, which is especially relevant in safety-centric applications. To that end, we propose a pipeline for a collaborative robot (Cobot) grasping algorithm that detects relevant tools and generates the optimal grasp. To increase the transparency and reliability of this approach, we integrate an explainable AI method that provides an explanation for the underlying prediction of a model by extracting the learned features and correlating them to corresponding classes from the input. These concepts are then used as additional criteria to ensure the safe handling of work tools. In this paper, we show the consistency of this approach and the criterion for improving the handover position. This approach was tested in an industrial environment, where a camera system was set up to enable a robot to pick up certain tools and objects.