Living organisms interact with their surroundings in a closed-loop fashion,
where sensory inputs dictate the initiation and termination of behaviours. Even
simple animals are able to develop and execute complex plans, which has not yet
been replicated in robotics using pure closed-loop input control. We propose a
solution to this problem by defining a set of discrete and temporary
closed-loop controllers, called "tasks", each representing a closed-loop
behaviour. We further introduce a supervisory module which has an innate
understanding of physics and causality, through which it can simulate the
execution of task sequences over time and store the results in a model of the
environment. On the basis of this model, plans can be made by chaining
temporary closed-loop controllers. The proposed framework was implemented for a
real robot and tested in two scenarios as proof of concept.

通过定义一组离散且临时的闭环控制器（称为 “任务”）以及引入一种对物理学和因果关系具有内在理解的监督模块，我们提出了一种解决在机器人技术中尚未实现的，使用纯闭环输入控制的复杂计划执行方法。我们通过模拟任务序列的执行并将结果存储在环境模型中，基于该模型构建计划，从而可以实现链式的临时闭环控制器。该框架已经在一个真实机器人上实施，并在两个场景中进行了概念验证。