A big challenge in human–robot interaction (HRI) is the design of autonomous robots that collaborate effectively with humans, exposing behaviors similar to those exhibited by humans when they interact with each other. Indeed, robots are part of daily life in multiple environments (i.e., cultural heritage sites, hospitals, offices, touristic scenarios and so on). In these contexts, robots have to coexist and interact with a wide spectrum of users not necessarily able or willing to adapt their interaction level to the kind requested by a machine: the users need to deal with artificial systems whose behaviors must be adapted as much as possible to the goals/needs of the users themselves, or more in general, to their mental states (beliefs, goals, plans and so on). In this paper, we introduce a cognitive architecture for adaptive and transparent human–robot interaction. The architecture allows a social robot to dynamically adjust its level of collaborative autonomy by restricting or expanding a delegated task on the basis of several context factors such as the mental states attributed to the human users involved in the interaction. This collaboration has to be based on different cognitive capabilities of the robot, i.e., the ability to build a user’s profile, to have a Theory of Mind of the user in terms of mental states attribution, to build a complex model of the context, intended both as a set of physical constraints and constraints due to the presence of other agents, with their own mental states. Based on the defined cognitive architecture and on the model of task delegation theorized by Castelfranchi and Falcone, the robot’s behavior is explainable by considering the abilities to attribute specific mental states to the user, the context in which it operates and its attitudes in adapting the level of autonomy to the user’s mental states and the context itself. The architecture has been implemented by exploiting the well known agent-oriented programming framework Jason. We provide the results of an HRI pilot study in which we recruited 26 real participants that have interacted with the humanoid robot Nao, widely used in HRI scenarios. The robot played the role of a museum assistant with the main goal to provide the user the most suitable museum exhibition to visit.

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协同自治：智能帮助测试的人机交互

人机交互 (HRI) 的一大挑战是设计与人类有效协作的自主机器人，暴露出与人类交互时所表现出的行为相似的行为。事实上，机器人是多种环境（即文化遗产、医院、办公室、旅游场景等）中日常生活的一部分。在这些情况下，机器人必须与广泛的用户共存并进行交互，这些用户不一定能够或愿意将其交互级别调整为机器所要求的类型：用户需要处理其行为必须尽可能多地适应的人工系统。可能取决于用户自己的目标/需求，或者更一般地说，可能取决于他们的心理状态（信念、目标、计划等）。在本文中，我们为自适应和透明的人机交互引入了认知架构。该架构允许社交机器人通过基于多个上下文因素（例如归因于参与交互的人类用户的心理状态）限制或扩展委派任务来动态调整其协作自治水平。这种协作必须基于机器人的不同认知能力，即建立用户档案的能力，在心理状态归因方面拥有用户的心理理论，建立复杂的上下文模型，旨在既是一组物理约束，又是由于其他代理的存在而产生的约束，具有自己的心理状态。基于定义的认知架构和由 Castelfranchi 和 Falcone 提出的任务委托模型，机器人的行为可以通过考虑将特定心理状态归因于用户的能力、它运行的环境以及它在使自主水平适应用户的心理状态和环境本身时的态度来解释。该架构是通过利用众所周知的面向代理的编程框架 Jason 来实现的。我们提供了 HRI 试点研究的结果，在该研究中，我们招募了 26 名真实参与者，他们与人形机器人 Nao 进行了交互，广泛用于 HRI 场景。机器人扮演博物馆助手的角色，主要目标是为用户提供最适合参观的博物馆展览。它运作的环境以及它在使自主水平适应用户的心理状态和环境本身方面的态度。该架构是通过利用众所周知的面向代理的编程框架 Jason 来实现的。我们提供了 HRI 试点研究的结果，在该研究中，我们招募了 26 名真实参与者，他们与人形机器人 Nao 进行了交互，广泛用于 HRI 场景。机器人扮演博物馆助手的角色，主要目标是为用户提供最适合参观的博物馆展览。它运作的环境以及它在使自主水平适应用户的心理状态和环境本身方面的态度。该架构是通过利用众所周知的面向代理的编程框架 Jason 来实现的。我们提供了 HRI 试点研究的结果，在该研究中，我们招募了 26 名真实参与者，他们与人形机器人 Nao 进行了交互，广泛用于 HRI 场景。机器人扮演博物馆助手的角色，主要目标是为用户提供最适合参观的博物馆展览。广泛用于 HRI 场景。机器人扮演博物馆助手的角色，主要目标是为用户提供最适合参观的博物馆展览。广泛用于 HRI 场景。机器人扮演博物馆助手的角色，主要目标是为用户提供最适合参观的博物馆展览。