(Georgia Institute of Technology, 2006)
Sun, J.; Mehta, Tejas R.; Wooden, David; Powers, Matthew; Rehg, James M.; Balch, Tucker; Egerstedt, Magnus B.
In this paper, we present a multi-pronged approach to the "Learning from Example"
problem. In particular, we present a framework for integrating learning
into a standard, hybrid navigation strategy, composed of both plan-based and
reactive controllers. Based on the classification of colors and textures as either
good or bad, a global map is populated with estimates of preferability
in conjunction with the standard obstacle information. Moreover, individual
feedback mappings from learned features to learned control actions are introduced
as additional behaviors in the behavioral suite. A number of real-world
experiments are discussed that illustrate the viability of the proposed method.