In social and service robotics, complex collaborative tasks are expected to be executed while interacting with humans in a natural and fluent manner. In this scenario, the robotic system is typically provided with structured tasks to be accomplished, but must also continuously adapt to human activities, commands, and interventions. We propose to tackle these issues by exploiting the concept of cognitive control, introduced in cognitive psychology and neuroscience to describe the executive mechanisms needed to support adaptive responses and complex goal-directed behaviors. Specifically, we rely on a supervisory attentional system to orchestrate the execution of hierarchically organized robotic behaviors. This paradigm seems particularly effective not only for flexible plan execution but also for human–robot interaction, because it directly provides attention mechanisms considered as pivotal for implicit, non-verbal human–human communication. Following this approach, we are currently developing a robotic cognitive control framework enabling collaborative task execution and incremental task learning. In this paper, we provide a uniform overview of the framework illustrating its main features and discussing the potential of the supervisory attentional system paradigm in different scenarios where humans and robots have to collaborate for learning and executing everyday activities.

中文翻译：

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用于协作任务执行和学习的机器人认知控制框架

在社交和服务机器人技术中，人们期望在以自然流畅的方式与人类交互的同时执行复杂的协作任务。在这种情况下，机器人系统通常具有要完成的结构化任务，但还必须不断适应人类活动、命令和干预。我们建议通过利用认知心理学和神经科学中引入的认知控制概念来解决这些问题，以描述支持适应性反应和复杂的目标导向行为所需的执行机制。具体来说，我们依靠监督注意系统来协调分层组织的机器人行为的执行。这种范式似乎不仅对灵活的计划执行而且对人机交互特别有效，因为它直接提供了被认为是隐含的、非语言的人与人之间交流的关键的注意力机制。按照这种方法，我们目前正在开发一个机器人认知控制框架，支持协作任务执行和增量任务学习。在本文中，我们提供了框架的统一概述，说明了其主要特征，并讨论了监督注意系统范式在人类和机器人必须协作学习和执行日常活动的不同场景中的潜力。