Context-aware collaborative pushing of heavy objects using skeleton-based intention prediction

TL;DR

This paper introduces a context-aware, skeleton-based intention prediction method using graph neural networks to improve human-robot collaboration in pushing heavy objects without force feedback, enhancing efficiency and reducing effort.

Contribution

The work presents a novel approach employing directed graph neural networks to analyze human posture for intention prediction in physical collaboration tasks.

Findings

Robot assistance reduces human effort in pushing tasks.

Posture-based intention prediction improves robot control accuracy.

Incorporating context recognition enhances collaboration efficiency.

Abstract

In physical human-robot interaction, force feedback has been the most common sensing modality to convey the human intention to the robot. It is widely used in admittance control to allow the human to direct the robot. However, it cannot be used in scenarios where direct force feedback is not available since manipulated objects are not always equipped with a force sensor. In this work, we study one such scenario: the collaborative pushing and pulling of heavy objects on frictional surfaces, a prevalent task in industrial settings. When humans do it, they communicate through verbal and non-verbal cues, where body poses, and movements often convey more than words. We propose a novel context-aware approach using Directed Graph Neural Networks to analyze spatio-temporal human posture data to predict human motion intention for non-verbal collaborative physical manipulation. Our experiments demonstrate that robot assistance significantly reduces human effort and improves task efficiency. The results indicate that incorporating posture-based context recognition, either together with or as an alternative to force sensing, enhances robot decision-making and control efficiency.