We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in realtime for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.
In order to inform the design of behaviors for robots that share domestic and public spaces with people, it is important to know what robot behavior is considered as normative. The work reported in this paper stems from the premise that what is perceived as socially normative behavior for people may differ from what is considered socially normative for a robot. This paper details the development of a data collection instrument, BEHAVE-II, for assessing user responses toward a robot's behavior using both attitudinal and behavioral responses. To test the validity and reliability of the BEHAVE-II instrument, a human-robot interaction experiment was conducted in which a robot or a human invaded the personal space of a participant. We found that participants' reactions were stronger when their personal space was invaded by a robot compared with a person. This points to the fact that humans are actually highly sensible whether robots' adhere to social norms which underlines the importance of the BEHAVE-II instrument.
People tend to unconsciously attribute personality traits to all kinds of technology including robots. But what personality do they want robots to have? Previous research has found support for two contradicting theories: similarity attraction and complementary attraction. The similarity attraction theory implies that people prefer a robot with a similar personality to their own (e.g., an extroverted person prefers an extroverted robot). According to the complementary attraction theory, people prefer a robot's personality opposite to their own (e.g., extroverted people prefer an introverted robot). In contrast to both theories, we argue that what is considered an appropriate personality for a robot depends on the task context. In a 2x2 between-groups experiment (N=45), we found trends that indicated similarity attraction for extrovert participants when the robot was a tour guide and complementary attraction for introverted participants when the robot was a cleaner. These trends show that preferences for robot personalities may indeed depend on the context of the robot's role and the stereotype perceptions people hold for certain jobs. Robot behaviors likely need to be adapted not in complimentary or similarity to the users' personality but to the users' expectations about what kind of personality and behaviors are consistent with such a task or role.
In our daily life everything and everyone occupies an amount of space, simply by "being there". Edward Hall coined the term proxemics for the studies of man's use of this space. This paper presents a study on proxemics in Human-Robot Interaction and particularly on robot's approaching groups of people. As social psychology research found proxemics to be culturally dependent, we focus on the question of the appropriateness of the robot's approach behavior in different cultures. We present an online survey (N=181) that was distributed in three countries; China, the U.S. and Argentina. Our results show that participants prefer a robot that stays out of people's intimate space zone just like a human would be expected to do. With respect to cultural differences, Chinese participants showed high-contact responses and believed closer approaches were appropriate compared to their U.S. counterparts. Argentinian participants more closely resembled the ratings of the U.S. participants.
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