  In trying to build humanoid robots that perform useful tasks in a world built
for humans, we address the problem of autonomous locomotion. Humanoid robot
planning and control algorithms for walking over rough terrain are becoming
increasingly capable. At the same time, commercially available depth cameras
have been getting more accurate and GPU computing has become a primary tool in
AI research. In this paper, we present a newly constructed behavior control
system for achieving fast, autonomous, bipedal walking, without pauses or
deliberation. We achieve this using a recently published rapid planar regions
perception algorithm, a height map based body path planner, an A* footstep
planner, and a momentum-based walking controller. We put these elements
together to form a behavior control system supported by modern software
development practices and simulation tools.
