Robotic Emotions

Abstract

The hypothesis that artificial emotion-like mechanisms can improve the adaptive performance of robots and intelligent systems has gained considerable support in recent years. To test this hypothesis, a mobile robot navigation system has been developed that employs affect and emotion as adaptation mechanisms. The robot’s emotions can arise from hard-coded interpretations of local stimuli, as well as from learned associations stored in global maps. They are expressed as modulations of planning and control parameters, and also as location-specific biases to path-planning. Our focus is on affective mechanisms that have practical utility rather than aesthetic appeal, so we present an extensive quantitative analysis of the system’s performance in a range of experimental situations.