Matarese Marco, Rea Francesco, Sciutti Alessandra
DIBRIS Department, University of Genoa, Genoa, Italy.
RBCS Unit, Italian Institute of Technology, Genoa, Italy.
Front Robot AI. 2022 Jun 15;9:733954. doi: 10.3389/frobt.2022.733954. eCollection 2022.
Partners have to build a shared understanding of their environment in everyday collaborative tasks by aligning their perceptions and establishing a common ground. This is one of the aims of shared perception: revealing characteristics of the individual perception to others with whom we share the same environment. In this regard, social cognitive processes, such as joint attention and perspective-taking, form a shared perception. From a Human-Robot Interaction (HRI) perspective, robots would benefit from the ability to establish shared perception with humans and a common understanding of the environment with their partners. In this work, we wanted to assess whether a robot, considering the differences in perception between itself and its partner, could be more effective in its helping role and to what extent this improves task completion and the interaction experience. For this purpose, we designed a mathematical model for a collaborative shared perception that aims to maximise the collaborators' knowledge of the environment when there are asymmetries in perception. Moreover, we instantiated and tested our model a real HRI scenario. The experiment consisted of a cooperative game in which participants had to build towers of Lego bricks, while the robot took the role of a suggester. In particular, we conducted experiments using two different robot behaviours. In one condition, based on shared perception, the robot gave suggestions by considering the partners' point of view and using its inference about their common ground to select the most informative hint. In the other condition, the robot just indicated the brick that would have yielded a higher score from its individual perspective. The adoption of shared perception in the selection of suggestions led to better performances in all the instances of the game where the visual information was not common to both agents. However, the subjective evaluation of the robot's behaviour did not change between conditions.
在日常协作任务中,伙伴们必须通过协调他们的认知并建立共同基础,来构建对所处环境的共同理解。这是共享认知的目标之一:向与我们共享同一环境的他人揭示个体认知的特征。在这方面,诸如共同关注和换位思考等社会认知过程构成了共享认知。从人机交互(HRI)的角度来看,机器人若能与人类建立共享认知并与伙伴对环境达成共同理解,将会从中受益。在这项工作中,我们想评估一个考虑到自身与伙伴之间认知差异的机器人,在其辅助角色中是否会更有效,以及这在多大程度上能改善任务完成情况和交互体验。为此,我们设计了一个用于协作共享认知的数学模型,旨在在存在认知不对称的情况下,最大化合作者对环境的了解。此外,我们在一个真实的人机交互场景中实例化并测试了我们的模型。实验包括一个合作游戏,参与者必须搭建乐高积木塔,而机器人扮演建议者的角色。具体来说,我们使用两种不同的机器人行为进行了实验。在一种情况下,基于共享认知,机器人通过考虑伙伴的观点并利用其对共同基础的推断来选择最具信息量的提示,从而给出建议。在另一种情况下,机器人只是从其个人角度指出能获得更高分数的积木。在视觉信息并非两个主体都共有的游戏所有实例中,在选择建议时采用共享认知都带来了更好的表现。然而,不同条件下对机器人行为的主观评价并没有变化。