Pfeiffer, S, Ebrahimian, D, Herse, S, Le, TN, Leong, S, Lu, B, Powell, K, Raza, SA, Sang, T, Sawant, I, Tonkin, M, Vinaviles, C, Vu, TD, Yang, Q, Billingsley, R, Clark, J, Johnston, B, Madhisetty, S, McLaren, N, Peppas, P, Vitale, J & Williams, MA 2018, 'UTS Unleashed! RoboCup@Home SSPL Champions 2019', RoboCup 2019: Robot World Cup XXIII, Robot World Cup, Springer International Publishing, Sydney, NSW, Australia, pp. 603-615.View/Download from: Publisher's site
This paper summarizes the approaches employed by Team UTS Unleashed! to take First Place in the 2019 RoboCup@Home Social Standard Platform League. First, our system architecture is introduced. Next, our approach to basic skills needed for a strong performance in the competition. We describe several implementations for tests participation. Finally our software development methodology is discussed.
Herse, S, Vitale, J, Ebrahimian, D, Tonkin, M, Ojha, S, Sidra, S, Johnston, B, Phillips, S, Gudi, SLKC, Clark, J, Judge, W & Williams, MA 2018, 'Bon Appetit! Robot Persuasion for Food Recommendation', ACM/IEEE International Conference on Human-Robot Interaction, ACM/IEEE International Conference on Human-Robot Interaction, ACM, Chicago, USA, pp. 125-126.View/Download from: Publisher's site
© 2018 Authors. The integration of social robots within service industries requires social robots to be persuasive. We conducted a vignette experiment to investigate the persuasiveness of a human, robot, and an information kiosk when offering consumers a restaurant recommendation. We found that embodiment type significantly affects the persuasiveness of the agent, but only when using a specific recommendation sentence. These preliminary results suggest that human-like features of an agent may serve to boost persuasion in recommendation systems. However, the extent of the effect is determined by the nature of the given recommendation.
Herse, S, Vitale, J, Tonkin, M, Ebrahimian, D, Ojha, S, Johnston, B, Judge, W & Williams, MA 2018, 'Do You Trust Me, Blindly? Factors Influencing Trust Towards a Robot Recommender System', RO-MAN 2018 The 27th IEEE International Symposium on Robot and Human Interactive Communication, IEEE International Symposium on Robot and Human Interactive Communication., IEEE, China, pp. 7-14.View/Download from: Publisher's site
© 2018 IEEE. When robots and human users collaborate, trust is essential for user acceptance and engagement. In this paper, we investigated two factors thought to influence user trust towards a robot: preference elicitation (a combination of user involvement and explanation) and embodiment. We set our experiment in the application domain of a restaurant recommender system, assessing trust via user decision making and perceived source credibility. Previous research in this area uses simulated environments and recommender systems that present the user with the best choice from a pool of options. This experiment builds on past work in two ways: first, we strengthened the ecological validity of our experimental paradigm by incorporating perceived risk during decision making; and second, we used a system that recommends a nonoptimal choice to the user. While no effect of embodiment is found for trust, the inclusion of preference elicitation features significantly increases user trust towards the robot recommender system. These findings have implications for marketing and health promotion in relation to Human-Robot Interaction and call for further investigation into the development and maintenance of trust between robot and user.