Exploring Spatial Perspective Taking in Human-Robot Interaction

Humans often rely on their perspective taking skills to thrive within the world's complex relations and connections. An adequate understanding of others' spatial perspectives can increase the quality of the interaction, not only perceptually but also cognitively. This thesis is dedicated to exploring children and adults' spatial perspective taking abilities emerging from the interaction with embodied and virtual robots in different contexts. While most previous approaches were limited to particular circumstances and targeted adults, the proposed approach developed a cognitive model incorporated in agents to foster perspective taking abilities in different contexts. The developments of the model also detail the processes used by humans to infer spatial connections from other's viewpoints and it is adaptable to add or remove processes based on the context of interaction.First, this thesis explores different interaction modalities and cognitive abilities through user studies with children. Each interaction outlines a set of components and processes required to develop a perspective taking model for robots and agents. The platform developed for the first study aims at evaluating the effect of a robot's non-verbal gestures, such as pointing, on children's joint attention during reading activity. The second study evaluates children's perspective adaptation to the robot in the context of collaborative activity. We expanded our observations to include children's first perspective choice, how they tried to accommodate the robot's perspective, and how they updated their mental model during the interaction. The third study explores children's spatial perspective taking abilities using game-based interaction and non-verbal channels.Inspired by previous studies, the thesis proposes a cognitive model that uses automatic and cognitive controlled processes to generate behaviors and decisions for the robot. The model focuses on processes linked with taking spatial perspectives and can be integrated into any agent architecture that deals with decision making and reasoning. The agent processes are then adapted to a new interaction scenario. Finally, the thesis evaluates the model through a user study with adult participants and within a virtual platform, a drastic change from the exploratory study designs caused by the pandemic. The final studies are designed as two-player games with two virtual robots that interact with each other in two contexts of competition and cooperation. In the competitive version of the game, the robot guided by the human plays against the robot equipped with the perspective taking model, while in the cooperative version, the robots guide each other to win the game as a team. We performed two between-subject studies with more than 180 adults to evaluate the participants' perception of a robot with complete perspective taking abilities, compared to one with limited abilities. Participants were more influenced by the robot's perspective taking abilities in the cooperative game compared to the competitive one, which was reflected in their ratings of the robot's intelligence and game fun. Experimental results on the model evaluation can open up future possibilities for exploring links between the perspective developments in children through cooperation and competition. Furthermore, the model can be extended to study other perceptional, cognitive, and affective dimensions, such as prosocial behavior and and transparency.

Dynamics of Human-Robot Interaction in Domestic Environments

Julia Fink

Domestic service robots are nowadays widely available on the consumer market. As such, robots have begun entering people’s homes and daily lives. However, it seems that the dissemination of domestic robots has not happened as easily and widespread as it was anticipated in the first place. Little is known about the reasons why because long-term studies of ordinary people using real robots in their homes are rare. To better understand how people interact, use and accept domestic robots, studies of human-robot interaction require ecologically valid settings and the user and their needs have to come into the focus. In this dissertation, we propose to investigate the dynamics of human-robot interaction in domestic environments. We first explore the field by means of a 6-month ethnographic study of nine households. We provided each of the households with a Roomba vacuum cleaning robot. Our motivation is to understand long-term acceptance and to identify factors that can promote and hinder the integration of a domestic service robot in different types of households. We would like to find out how people’s perception of the robot, and the way they interact with it and use it, evolve over time. Furthermore, as social factors were highlighted in previous studies on technology adoption in homes, we shed light on to what extent people view Roomba and other types of domestic robots as a social entity and to what extent they anthropomorphize it. Findings of this research can be used to guide the design of user-oriented robots that have the potential to lastingly become a valuable part within the home ecology. Then, we pursue the idea of developing our own domestic robot prototype that could be used in a household with children. We imagine a playful robot that aims to motivate young children to tidy up their toys. In a first evaluation of the robot in 14 family homes, we study the effect of a proactive and reactive robot behavior on children’s interaction with the robot and their motivation to tidy up. A follow-up experiment explores the possibility to sustain children’s engagement by manipulating the robot’s behavior in such way that it appears unexpected. We further investigate how far this influences children’s perception of the robot in terms of anthropomorphism. Our findings emphasize the importance of research in ecologically valid settings in order to obtain a better understanding of human-robot interaction, advance further the design of user-oriented robots and foster the long-term acceptance of these devices.

EPFL2014

Learning How to Write with a Social Robot

Shruti Chandra

Learning handwriting is one of the main occupations for children in primary education. It is a complex skill that has been associated with children’s learning and development. Poor handwriting skills affect children negatively concerning their academic performance and motivation. In schools, children with handwriting problems need extra support and one-to-one interventions from teachers. Personalised one-to-one support requires additional resources that most schools generally cannot afford. Therefore, there is a need to explore different methods of learning and contemporary technologies to develop new educational tools to foster children’s learning process of handwriting. The research presented in this thesis addresses the question of - “How to create a social robot that can help children to acquire handwriting skills?". Thus, we looked into three different research domains: education; pedagogic sciences; and social human-robot interaction (HRI). On the one hand, we investigated the types of errors commonly present in children’s handwriting. On the other hand, we needed to explore different educational theories to find alternate learning methods that would maximise children’s learning gains. In addition, we investigated various possibilities for integrating a social robot into educational scenarios and measured its impact on the children’s learning process. This thesis presents five studies that follow an incremental evolution of the research. The first three studies were based on the interaction between two children, in the presence of either a human or a robotic facilitator, with the children performing a collaborative writing activity. We explored the ‘peer-learning’ and ‘peer-tutoring’ methods and compared the differences in terms of interaction behaviour and learning gains. Further, we analysed children’s interpersonal distance and feelings of responsibility towards their peers, both in the presence of a robot and human facilitator. Finally, we examined children’s assessment behaviour towards their peers and self-disclosure towards the robotic facilitator. The outcomes of these first studies provided enough data to generate a taxonomy of children handwriting errors to be used in the subsequent studies. They hinted that the ‘peer-tutoring’ method had a positive influence on children’s learning and corrective feedback to their peers in the presence of the social robot, providing positive support to the applicability of social robots as ‘facilitators’ in handwriting learning. The next step was to study the effects of the robot in the peer-tutoring scenario when assuming the role of a peer, directly interacting and performing a collaborative writing activity with the children. Thus, we developed an educational system with an autonomous social behaviour of a robot exhibiting various levels of learning competencies. The tutor-child provides corrective feedback on the writing of the learner-robot and in turn, enhances his writing skills. The robot’s writing was generated by an algorithm incorporating human-inspired movements and could reproduce a set of writing errors from the error taxonomy. We tested the system by conducting two longitudinal studies, each of them composed of four sessions. In the fourth study, we assigned the robot two contrasting competencies: ‘learning’ and ‘non-learning’. We measured the differences in children’s learning gains and changes in their perceptions towards the learner-robot. The fifth study followed a similar interaction scenario and research questions, but this time the robot performed three learning competencies: ‘continuous-learning’;’non-learning’ and ‘personalised-learning’. The findings of these studies show that the children improved more with the robot exhibiting learning, continuous-learning and personalised-learning competencies compared to the robot showing non-learning competency. The way children’s learning and perception of the robot evolved over time confirmed the need for multiple sessions for the last two studies. This thesis presents some evidence that social robots can be successfully used as tools for enhancing children’s handwriting skills in peer-learning scenarios.

EPFL2019

Learning How to Write with a Social Robot

Shruti Chandra

EPFL2019

Dynamics of Human-Robot Interaction in Domestic Environments

Julia Fink

EPFL2014