Developing Affect-Modulated Behaviors : Stability, Exploration, Exploitation or Imitation

Abstract

Exploring the environment is essential for autonomous agents to learn new things and to consolidate past experiences and apply them to improve behavior. However, exploration is also risky as it exposes the agent to unknown, potentially overwhelming or dangerous situations. A trade-off must hence exist between activities such as seeking stability, autonomous exploration of the environment, imitation of novel actions performed by another agens, and taking advantage of opportunities offered by new situations and events. In this paper, we present a Perception-Action robotic architecture that achieves this tradeoff on the grounds of modulatory mechanisms based on notions of “well-being” and “affect”. We have implemented and tested this architecture using a Koala robot, and we present and discuss behavior of the robot in different contexts