  Recent literature in the robotics community has focused on learning robot
behaviors that abstract out lower-level details of robot control. To fully
leverage the efficacy of such behaviors, it is necessary to select and sequence
them to achieve a given task. In this paper, we present an approach to both
learn and sequence robot behaviors, applied to the problem of visual navigation
of mobile robots. We construct a layered representation of control policies
composed of low- level behaviors and a meta-level policy. The low-level
behaviors enable the robot to locomote in a particular environment while
avoiding obstacles, and the meta-level policy actively selects the low-level
behavior most appropriate for the current situation based purely on visual
feedback. We demonstrate the effectiveness of our method on three simulated
robot navigation tasks: a legged hexapod robot which must successfully traverse
varying terrain, a wheeled robot which must navigate a maze-like course while
avoiding obstacles, and finally a wheeled robot navigating in the presence of
dynamic obstacles. We show that by learning control policies in a layered
manner, we gain the ability to successfully traverse new compound environments
composed of distinct sub-environments, and outperform both the low-level
behaviors in their respective sub-environments, as well as a hand-crafted
selection of low-level policies on these compound environments.
