Comparing Two Action Planning Approaches for Color Learning on a Mobile Robot

A major challenge to the deployment of mobile robots in a wide range of tasks is the ability to function autonomously, learning appropriate models for environmental features and adapting these models in response to environmental changes. Such autonomous operation is feasible iff the robot is able to plan an appropriate action sequence. In this paper, we focus on the task of color modeling/learning, and present two algorithms that enable a mobile robot to plan action sequences that facilitate color learning. We propose a long-term action-selection approach that maximizes color learning opportunities while minimizing localization errors over an entire action sequence, and compare it with a greedy/heuristic action-selection approach that plans incrementally, to maximize the utility based on the current state information. We show that long-term action-selection provides a more principled solution that requires minimal human supervision. All algorithms are fully implemented and tested on the Sony AIBO robots.