用物理引擎辅助机器人分步规划，实现动态避障
Closed-loop multi-step planning with innate physics knowledge
- 底层任务由传感器触发，形成临时闭环控制
- 高层配置器利用物理引擎模拟任务序列并生成有效计划
- 在真实机器人上验证了超车场景的可行性
我们提出一种分层框架，将机器人规划问题视为输入控制问题。底层为临时闭合控制回路（称为“任务”），每个任务对应特定感知输入下的行为，具有时效性。顶层由“配置器”负责任务的创建与终止，其中嵌入物理引擎作为核心知识库，可模拟任务序列。配置器根据模拟结果编码并解读信息，进而选择一组任务序列作为完整规划。我们在真实机器人上实现了该框架，并在超车场景中进行了概念验证。
原文摘要 · Abstract (English)
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.
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