Long-Term vs. Greedy Action Planning for Color Learning on a Mobile Robot

A major challenge to the deployment of mobile robots is the ability to function autonomously, learning appropriate models for environmental features and adapting those models in response to environmental changes. This autonomous operation in turn requires autonomous selection/planning of an action sequence that facilitates learning and adaptation. Here we focus on color modeling/learning and analyze two algorithms that enable a mobile robot to plan action sequences that facilitate color learning: a long-term action selection approach that maximizes color learning opportunities while minimizing localization errors over an entire action sequence, and a greedy/heuristic action selection approach that plans incrementally, one step at a time, to maximize the benefits based on the current state of the world. The long-term action selection results in a more principled solution that requires minimal human supervision, while better failure recovery is achieved by incorporating features of the greedy planning approach. All algorithms are fully implemented and tested on the Sony AIBO robots.