Effects of adapting to user pitch on rapport perception, behavior, and state with a social robotic learning companion

Author(s):  
Nichola Lubold ◽  
Erin Walker ◽  
Heather Pon-Barry
Author(s):  
Masashi Hamaya ◽  
Kazutoshi Tanaka ◽  
Yoshiya Shibata ◽  
Felix Wolf Hans Erich Von Drigalski ◽  
Chisato Nakashima ◽  
...  
Keyword(s):  

Author(s):  
Jiancong Huang ◽  
Juan Rojas ◽  
Matthieu Zimmer ◽  
Hongmin Wu ◽  
Yisheng Guan ◽  
...  
Keyword(s):  

2021 ◽  
Author(s):  
Katie Riddoch ◽  
Roxanne Hawkins ◽  
Emily S. Cross

To facilitate long-term engagement with social robots, emerging evidence suggests that modelling robots on social animals with whom many people form enduring social bonds – specifically, pet dogs – may be useful. However, scientific understanding of the features of pet dogs that are important for establishing and maintaining social bonds remains limited to broad qualities that are liked, as opposed to specific behaviours. To better understand dog behaviours that are perceived as important for facilitating social bonds between owner and pet, we surveyed current dog owners (n = 153) with open-ended questions about their dogs’ behaviours. Thematic analysis identified 7 categories of behaviours perceived as important to human-dog bonding, including: 1) attunement, 2) communication, 3) consistency and predictability, 4) physical affection, 5) positivity and enthusiasm, 6) proximity, and 7) shared activities. We consider the feasibility of translating these behaviours into a social robotic platform, and signpost potential barriers moving forward. In addition to providing insight into important behaviours for human—dog bonding, this work provides a springboard for those hoping to implement dog behaviours into animal-like artificial agents designed for social roles.


2013 ◽  
pp. 231-256
Author(s):  
Renato Ramos da Silva ◽  
Roseli Aparecida Francelin Romero

Computer vision is essential to develop a social robotic system capable to interact with humans. It is responsible to extract and represent the information around the robot. Furthermore, a learning mechanism, to select correctly an action to be executed in the environment, pro-active mechanism, to engage in an interaction, and voice mechanism, are indispensable to develop a social robot. All these mechanisms together provide a robot emulate some human behavior, like shared attention. Then, this chapter presents a robotic architecture that is composed with such mechanisms to make possible interactions between a robotic head with a caregiver, through of the shared attention learning with identification of some objects.


2020 ◽  
pp. 105971232092474
Author(s):  
André Cyr ◽  
Julie Morand-Ferron ◽  
Frédéric Thériault

Spatial information can be valuable, but new environments may be perceived as risky and thus often evoke fear responses and risk-averse exploration strategies such as thigmotaxis or wall-following behavior. Individual differences in risk-taking (boldness) and thigmotaxis have been reported in natural taxa, which may benefit their survival. In neurorobotic, the common approach is to reproduce cognitive phenomena with multiple levels of bio-inspiration into robotic scenarios. Since autonomous robots may benefit from these different behaviors in exploration tasks, this study aims at simulating two exploration strategies in a virtual robot controlled by a spiking neural network. The experimental context consists in a visual learning task solved through an operant conditioning procedure. Results suggest that the proposed neural architecture sustains both behaviors, switching from one to the other by external cues. This original bio-inspired model could be used as a first step toward further investigations of neurorobotic personality modulated by learning and complex exploration contexts.


Author(s):  
Masato Kotake ◽  
◽  
Daisuke Katagami ◽  
Katsumi Nitta ◽  

We focus on robotic learning under multiple instructors. Even when their goal is the same, different instructors inevitably was different approaches. We propose incorporating DP matching and clustering, classifying the teaching demonstrations of instructors into groups of similar ones. Experiments in which an AIBO robot was taught to walk forward demonstrated that our proposal acquired appropriate teaching approaches based on AIBO’s different embodiments and maximizing task accomplishment.


Author(s):  
Mauro Dragone ◽  
Joe Saunders ◽  
Kerstin Dautenhahn

AbstractEnabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the user’s acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree – to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving users’ needs, but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.


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