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：生物体以闭环方式与其环境互动，其中感觉输入决定行为的启动与终止。即使是简单动物也能制定并执行复杂计划，这在机器人学中尚未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"）来解决该问题，每个控制器代表一种闭环行为。我们进一步引入具有先天物理与因果理解能力的监督模块，该模块能够模拟任务序列随时间推移的执行过程，并将结果存储于环境模型中。基于此模型，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，并在两个场景中进行了概念验证测试。