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.

翻译：生命有机体以闭环方式与其周围环境互动，其中感觉输入决定了行为的发起与终止。即使是简单的动物也能制定并执行复杂的计划，而这在机器人学中尚无法通过纯闭环输入控制实现。我们通过定义一组离散且临时的闭环控制器（称为“任务”，每个任务代表一种闭环行为）来提出对此问题的解决方案。我们进一步引入一个具备先天物理与因果理解的监督模块，通过该模块，它可以模拟任务序列随时间的执行过程，并将结果存储于环境模型中。基于该模型，可通过链接临时闭环控制器来制定计划。所提出的框架已在真实机器人上实现，并在两个场景中作为概念验证进行了测试。