Autonomous planned color learning on a legged robot

Abstract

Our research focuses on automating the color-learning process on-board a legged robot with limited computational and memory resources. A key defining feature of our approach is that instead of using explicitly labeled training data it trains autonomously and incrementally, thereby making it: robust to re-colorings in the environment. Prior results demonstrated the ability of the robot to learn a color map when given an executable motion sequence designed to present it with good color-learning opportunities based on the known structure of its environment. This paper extends these results by demonstrating that the robot can plan its own such motion sequence and perform just as well at color-learning. The knowledge acquired at each stage of the learning process is used as a bootstrap mechanism to aid the robot in planning its motion during subsequent stages. © Springer-Verlag Berlin Heidelberg 2007.