Augmenting Robot Behaviors Using Physiological Measures

In recent years, advancements in Unmanned Systems have allowed Human Robot Interaction (HRI) to transition from direct remote control to autonomous systems capable of self-navigation. However, these new technologies do not yet support true mixed-initiative solider-robot teaming where soldiers work with another agent as if it were another human being. In order to achieve this goal, researchers must explore new types of multi-modal and natural communication strategies and methods to provide robots improved understanding of their human counterparts\u27 thought process. Physiological sensors are continuously becoming more portable and affordable leading to the possibility of providing new insight of team member state to a robot team member. However, steps need to be taken to improve how affective and cognitive states are measured and how these new metrics can be used to augment the decision making process for a robot team member. This paper describes current state of the art and next steps needed for accurate profile creation for improved human robot team performance. © 2011 Springer-Verlag

Augmenting Robot Behaviors Using Physiological Measures Of Workload State

The evolution of robots from tools to teammates requires a paradigm shift. Robot teammates need to interpret naturalistic forms of human communication and sense implicit, but important cues that reflect the human teammate’s psychological state. A closed-loop system where the robot teammate detects the human teammate’s workload state would enable the robot to select appropriate aiding behaviors to support its human teammate. Physiological measures are suitable for assessment of workload in adaptive systems because they allow continuous assessment and do not require overt responses which disrupt tasks. Given the large variability in physiological workload responses across individuals, an algorithm that accommodates variability in workload responses would be more robust. This study outlines the development and validation of algorithms for workload classification. It discusses (i) a workload manipulation paradigm, (ii) the evaluation of the algorithms for deriving a workload index that is individualized, and (iii) parameter selection for optimal classification