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.

翻译：在为人类建造的世界中,我们试图建设执行有益任务的人类机器人,我们解决了自主移动问题。人类机器人在粗野地形上行走的规划和控制算法越来越有能力。与此同时,商业上可以得到的深度相机越来越准确,而GPU计算已成为AI研究的主要工具。在本文中,我们提出了一个新建立的行为控制系统,用于实现快速、自主、双脚行走,没有停顿或考虑。我们利用最近出版的快速规划区域感知算法、一个基于高度地图的人体路径规划仪、一个A*脚步规划仪和一个基于动力的行走控制器来实现这一目标。我们把这些要素放在一起形成了一个行为控制系统,得到现代软件开发做法和模拟工具的支持。