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Comparing task-based and socially intelligent behaviour in a robot bartender
- In Proceedings of the 15th International Conference on Multimodal Interfaces (ICMI 2013
, 2013
"... We address the question of whether service robots that interact with humans in public spaces must express socially appropriate behaviour. To do so, we implemented a robot bartender which is able to take drink orders from humans and serve drinks to them. By using a high-level automated planner, we ex ..."
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Cited by 7 (7 self)
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We address the question of whether service robots that interact with humans in public spaces must express socially appropriate behaviour. To do so, we implemented a robot bartender which is able to take drink orders from humans and serve drinks to them. By using a high-level automated planner, we explore two different robot interaction styles: in the task only setting, the robot simply fulfils its goal of asking customers for drink orders and serving them drinks; in the socially intelligent setting, the robot additionally acts in a manner so-cially appropriate to the bartender scenario, based on the behaviour of humans observed in natural bar interactions. The results of a user study show that the interactions with the socially intelligent robot were somewhat more efficient, but the two implemented be-haviour settings had only a small influence on the subjective ratings. However, there were objective factors that influenced participant ratings: the overall duration of the interaction had a positive influ-ence on the ratings, while the number of system order requests had a negative influence. We also found a cultural difference: German participants gave the system higher pre-test ratings than participants who interacted in English, although the post-test scores were similar.
Handling uncertain input in multi-user human-robot interaction
- in Proceedings RO-MAN 2014
, 2014
"... Abstract — In this paper we present results from a user evaluation of a robot bartender system which handles state uncertainty derived from speech input by using belief tracking and generating appropriate clarification questions. We present a combination of state estimation and action selection comp ..."
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Abstract — In this paper we present results from a user evaluation of a robot bartender system which handles state uncertainty derived from speech input by using belief tracking and generating appropriate clarification questions. We present a combination of state estimation and action selection components in which state uncertainty is tracked and exploited, and compare it to a baseline version that uses standard speech recognition confidence score thresholds instead of belief track-ing. The results suggest that users are served fewer incorrect drinks when the uncertainty is retained in the state. I.
Edinburgh (UK)
"... Training and evaluation of an MDP model for social multi-user ..."
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Evaluating a social multi-user interaction model using a Nao robot
"... Abstract — This paper presents results from a user evaluation of a robot bartender system, which supports social engagement and interaction with multiple customers. The system is a Nao-based alternative version of an existing robot bartender developed in the JAMES project [1]. The Nao-based version ..."
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Abstract — This paper presents results from a user evaluation of a robot bartender system, which supports social engagement and interaction with multiple customers. The system is a Nao-based alternative version of an existing robot bartender developed in the JAMES project [1]. The Nao-based version has given us a local experimentation platform, allowing us to focus on social multi-user interaction rather than the robot technology of object manipulation. We will describe the design of the Nao-based system and discuss the differences with the original JAMES system. In a recent evaluation of the JAMES system with real users, a trained and a hand-coded version of the action selection policy were compared [2]. Here we present results from a similar comparative user evaluation on the Nao-based system, which confirm the conclusions of the previous experiment and provide further evidence in favour of the trained action selection mechanism. Task success was found to be almost 20 % higher with the trained policy, with interaction times being about 10 % shorter. Participants also rated the trained system as significantly more natural, more understanding, and better at providing appropriate attention. I.
model implemented on a Nao robot
"... evaluation of a multi-user social interaction ..."
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