Ranieri Caetano M, Moioli Renan C, Vargas Patricia A, Romero Roseli A F
Institute of Mathematical and Computer Sciences, University of Sao Paulo, Avenida Trabalhador Sao Carlense, 400, Sao Carlos, SP 13566-590 Brazil.
Bioinformatics Multidisciplinary Environment (BioME), Digital Metropolis Institute, Federal University of Rio Grande do Norte, Avenida Senador Salgado Filho, 3000, Natal, RN 59078-970 Brazil.
Cogn Neurodyn. 2023 Aug;17(4):1009-1028. doi: 10.1007/s11571-022-09886-z. Epub 2022 Sep 27.
Behaviour selection has been an active research topic for robotics, in particular in the field of human-robot interaction. For a robot to interact autonomously and effectively with humans, the coupling between techniques for human activity recognition and robot behaviour selection is of paramount importance. However, most approaches to date consist of deterministic associations between the recognised activities and the robot behaviours, neglecting the uncertainty inherent to sequential predictions in real-time applications. In this paper, we address this gap by presenting an initial neurorobotics model that embeds, in a simulated robot, computational models of parts of the mammalian brain that resembles neurophysiological aspects of the basal ganglia-thalamus-cortex (BG-T-C) circuit, coupled with human activity recognition techniques. A robotics simulation environment was developed for assessing the model, where a mobile robot accomplished tasks by using behaviour selection in accordance with the activity being performed by the inhabitant of an intelligent home. Initial results revealed that the initial neurorobotics model is advantageous, especially considering the coupling between the most accurate activity recognition approaches and the computational models of more complex animals.
行为选择一直是机器人技术领域的一个活跃研究课题，尤其是在人机交互领域。为了使机器人能够自主且有效地与人类进行交互，人类活动识别技术与机器人行为选择之间的耦合至关重要。然而，迄今为止的大多数方法都由已识别活动与机器人行为之间的确定性关联组成，忽略了实时应用中顺序预测所固有的不确定性。在本文中，我们通过提出一个初始神经机器人模型来弥补这一差距，该模型在模拟机器人中嵌入了哺乳动物大脑部分的计算模型，该模型类似于基底神经节 - 丘脑 - 皮质（BG - T - C）回路的神经生理学方面，并结合了人类活动识别技术。开发了一个机器人模拟环境来评估该模型，在该环境中，移动机器人通过根据智能家居居住者正在执行的活动进行行为选择来完成任务。初步结果表明，初始神经机器人模型具有优势，特别是考虑到最精确的活动识别方法与更复杂动物的计算模型之间的耦合。
