Implications of Personality on Cognitive Workload, Affect, and Task Performance in Robot Remote Control

This paper explores how the personality traits of robot operators can impact their task performance during remote control of robots. The influence of personal dispositions on information processing, either directly or indirectly, needs to be examined when working with robots on specific tasks. To investigate this relationship, we utilize the open-access multi-modal dataset MOCAS to examine the operator's personality, affect, cognitive load, and task performance. Our objective is to confirm if personal traits have a total effect, including both direct and indirect effects, that could significantly impact operator performance level. We specifically examine the relationship between personality traits such as extroversion, conscientiousness, and agreeableness, and task performance. We analyze the correlation between cognitive load, self-ratings of workload and affect, and the quantified individual personality traits and their experimental scores. As a result, we confirm that personality traits have no total effect on task performance.

A ROS-based Framework for Monitoring Human and Robot Conditions in a Human-Multi-robot Team

This paper presents a framework for monitoring human and robot conditions in human multi-robot interactions. The proposed framework consists of four modules: 1) human and robot conditions monitoring interface, 2) synchronization time filter, 3) data feature extraction interface, and 4) condition monitoring interface. The framework is based on Robot Operating System (ROS), and it supports physiological and behavioral sensors and devices and robot systems, as well as custom programs. Furthermore, it allows synchronizing the monitoring conditions and sharing them simultaneously. In order to validate the proposed framework, we present experiment results and analysis obtained from the user study where 30 human subjects participated and simulated robot experiments.