We present a hierarchical framework to solve robot planning as an input control problem. At the lowest level are temporary closed control loops, ("tasks"), each representing a behaviour, contingent on a specific sensory input and therefore temporary. At the highest level, a supervising "Configurator" directs task creation and termination. Here resides "core" knowledge as a physics engine, where sequences of tasks can be simulated. The Configurator encodes and interprets simulation results,based on which it can choose a sequence of tasks as a plan. We implement this framework on a real robot and test it in an overtaking scenario as proof-of-concept.

翻译：我们提出了一种分层框架，将机器人规划作为输入控制问题求解。最底层是临时的闭环控制回路（“任务”），每个回路代表一种行为，依赖于特定的感官输入，因此具有临时性。最高层由一个监督性的“配置器”指导任务的创建与终止。该层集成了作为物理引擎的“核心”知识，可对任务序列进行模拟。配置器对仿真结果进行编码与解释，并据此选择任务序列作为规划方案。我们在真实机器人上实现了该框架，并以超车场景作为概念验证进行了测试。