Toward Mutual Trust Modeling in Human-Robot Collaboration

TL;DR

This paper proposes a real-time mutual trust model for human-robot collaboration that considers trust levels of both parties, aiming to enhance interaction naturalness and human autonomy.

Contribution

It introduces a novel trust model capturing mutual trust in real-time, addressing a gap in existing research focused mainly on human trust in machines.

Findings

Trust levels of both human and robot are modeled in real-time.

The model enables smoother and more natural human-robot interactions.

It reduces the need for humans to constantly monitor robot behavior.

Abstract

The recent revolution of intelligent systems made it possible for robots and autonomous systems to work alongside humans, collaborating with them and supporting them in many domains. It is undeniable that this interaction can have huge benefits for humans if it is designed properly. However, collaboration with humans requires a high level of cognition and social capabilities in order to gain humans acceptance. In all-human teams, mutual trust is the engine for successful collaboration. This applies to human-robot collaboration as well. Trust in this interaction controls over- and under-reliance. It can also mitigate some risk. Therefore, an appropriate trust level is essential for this new form of teamwork. Most research in this area has looked at trust of humans in machines, neglecting the mutuality of trust among collaboration partners. In this paper, we propose a trust model that incorporates this mutuality captures trust levels of both the human and the robot in real-time, so that robot can base actions on this, allowing for smoother, more natural interactions. This increases the human autonomy since the human does not need to monitor the robot behavior all the time.