Exploring the Design Space of Robot Appearance and Behavior in an Attention-Seeking Living Room Scenario for a Robot Companion

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What is a robot companion - friend, assistant or butler?

The study presented in this paper explored people's perceptions and attitudes towards the idea of a future robot companion for the home. A human-centred approach was adopted using questionnaires and human-robot interaction trials to derive data from 28 adults. Results indicated that a large proportion of participants were in favour of a robot companion and saw the potential role as being an assistant, machine or servant. Few wanted a robot companion to be a friend. Household tasks were preferred to child/animal care tasks. Humanlike communication was desirable for a robot companion, whereas humanlike behaviour and appearance were less essential. Results are discussed in relation to future research directions for the development of robot companions

Social robots for older users: a possibility to support assessment and social interventions

In the last decades, various researches in the field of robotics have created numerous opportunities for innovative support of the older population. The goal of this work was to review and highlight how social robots can help the daily life of older people, and be useful also as assessment tools. We will underline the aspects of usability and acceptability of robotic supports in the psychosocial work with older persons. The actual usability of the system influences the perception of the ease of use only when the user has no or low experience, while expert users’ perception is related to their attitude towards the robot. This finding should be more deeply analysed because it may have a strong influence on the design of future interfaces for elderly-robot interaction. Robots can play an important role to tackle the societal challenge of the growing older population. The authors report some recent studies with older users, where it was demonstrated that the acceptability of robotics during daily life activities, and also in cognitive evaluation, could be supported by social robot

People tracking and re-identification by face recognition for RGB-D camera networks

This paper describes a face recognition-based people tracking and re-identification system for RGB-D camera networks. The system tracks people and learns their faces online to keep track of their identities even if they move out from the camera's field of view once. For robust people re-identification, the system exploits the combination of a deep neural network- based face representation and a Bayesian inference-based face classification method. The system also provides a predefined people identification capability: it associates the online learned faces with predefined people face images and names to know the people's whereabouts, thus, allowing a rich human-system interaction. Through experiments, we validate the re-identification and the predefined people identification capabilities of the system and show an example of the integration of the system with a mobile robot. The overall system is built as a Robot Operating System (ROS) module. As a result, it simplifies the integration with the many existing robotic systems and algorithms which use such middleware. The code of this work has been released as open-source in order to provide a baseline for the future publications in this field

An empirical framework for human-robot proxemics

The work described in this paper was conducted within the EU Integrated Projects COGNIRON ("The Cognitive Robot Companion") and LIREC (LIving with Robots and intEractive Companions) and was funded by the European Commission under contract numbers FP6- 002020 and FP7-215554.An empirical framework for Human-Robot (HR) proxemics is proposed which shows how the measurement and control of interpersonal distances between a human and a robot can be potentially used by the robot to interpret, predict and manipulate proxemic behaviour for Human-Robot Interactions (HRIs). The proxemic framework provides for incorporation of inter-factor effects, and can be extended to incorporate new factors, updated values and results. The framework is critically discussed and future work proposed