Assistance-Seeking in Human-Supervised Autonomy: Role of Trust and Secondary Task Engagement (Extended Version)

TL;DR

This paper investigates how trust and engagement influence human assistance-seeking in human-supervised robotic tasks, using a dual-task paradigm and modeling to optimize assistance policies.

Contribution

It introduces a linear dynamical system model of trust and engagement and applies Model Predictive Control to optimize assistance-seeking strategies in human-robot interaction.

Findings

Participants interrupt robots more when trust and engagement are low.

MPC-based assistance policy outperforms baseline in most cases.

Model effectively predicts human reliance and trust dynamics.

Abstract

Using a dual-task paradigm, we explore how robot actions, performance, and the introduction of a secondary task influence human trust and engagement. In our study, a human supervisor simultaneously engages in a target-tracking task while supervising a mobile manipulator performing an object collection task. The robot can either autonomously collect the object or ask for human assistance. The human supervisor also has the choice to rely upon or interrupt the robot. Using data from initial experiments, we model the dynamics of human trust and engagement using a linear dynamical system (LDS). Furthermore, we develop a human action model to define the probability of human reliance on the robot. Our model suggests that participants are more likely to interrupt the robot when their trust and engagement are low during high-complexity collection tasks. Using Model Predictive Control (MPC), we design an optimal assistance-seeking policy. Evaluation experiments demonstrate the superior performance of the MPC policy over the baseline policy for most participants.