EMOTION-TRIGGERED LEARNING IN AUTONOMOUS ROBOT CONTROL

Abstract

The fact that emotions are considered to be essential to human reasoning suggests that they might play an important role in autonomous robots as well. In particular, the decision of when to interrupt ongoing behavior is often associated with emotions in natural systems. The question under examination here is whether this role of emotions can be useful for a robot which adapts to its environment. For this purpose, an emotion model was developed and integrated in a reinforcement-learning framework. Robot experiments were done to test an emotion-dependent mechanism for the automatic detection of the relevant events of a learning task against more traditional approaches. Experimental results are presented that confirm that emotions can be useful in this role, specifically by improving the efficiency of the learning algorithm.