The physical actions performed by any robot can be used to convey meaning to a user in human-robot interaction. While the analysis of physical actions as communicative acts is not new, it is less clear how dialogue planning policies for human-robot interaction should be influenced by the co-occurrence of physical tasks actions. In this short paper we report on a study which analyses the relative importance of omitting verbal feedback in situated human-robot dialogue. Results indicate that while a lack of explicit feedback can and does lead to more errors in dialogue, overall task performance times are improved, while users perceive the resultant system as better performing on a number of subjective measures.

Investigates the potential role of `basic communicative acts' (pointing gestures, yes/no signs, object-offer, object-request, attention-getting signals, and many others) for human-robot interaction. Argues that BCAs constitute a strategy for implementing natural HRI because they are (1) non-linguistic, hence simpler than language-based signals, (2) while simultaneously permitting sophisticated human-robot cooperation. Draws on evolutionary theory and research on embodied cognition (`affordances'). Discusses some of the challenges involved in implementing embodied systems that comprehend BCAs. The chief problem will be to design systems that use context.

Not all questions are asked with the same intention. Humans tend to address the implicit meaning of the question (that contributes to its pragmatic force), which requires knowledge of the context and a degree of common ground, more so than addressing the explicit propositional content of the question. Is recognizing the pragmatic force in today's human-robot dialogue systems worth the trouble? We focus on display questions (questions to which the asker already knows the answer) and argue that there are realistic human-robot interaction scenarios in existence today that would benefit from the deeper intention recognition. We also propose a method for obtaining display question annotations by embedding an elicitation question into the dialogue. The preliminary study of our robot receptionist shows that at least 16.7% of interactions with the embedded elicitation question include a display question.

An important difficulty in developing spoken dialogue systems for robots is the open-ended nature of most interactions. Robotic agents must typically operate in complex, continuously changing environments which are difficult to model and do not provide any clear, predefined goal. Directly capturing this complexity in a single, large dialogue policy is thus inadequate. This paper presents a new approach which tackles the complexity of open-ended interactions by breaking it into a set of small, independent policies, which can be activated and deactivated at runtime by a dedicated mechanism. The approach is currently being implemented in a spoken dialogue system for autonomous robots.

Through empirical validation and computational application, template-based models of situated spatial term meaning have proven their usefulness to human-robot dialogue, but we argue in this paper that important contextual features are being ignored; resulting in over-generalization and failure to account for actual usage in situated context. Such a fact is significant to human-robot dialogue in that it constrains the manner in which we create interactive systems which can discuss their own physical actions and surroundings. To this end, in this paper we describe a study which we conducted to determine how acceptability ratings for spatial term meaning altered for oblique landmark orientations. Results demonstrated that spatial term meaning was indeed altered by interlocutor perspective in a way not predicted by current approaches to spatial term semantics.

Applications in Human-Robot Interaction (HRI) in the not-so-distant future include robots that collaborate with factory workers or serve us as caregivers or waitstaff. When offering customized functionality in these dynamic environments, robots need to engage in real-time exchanges with humans. Robots thus need to be capable of participating in smooth turn-taking interactions. The research goal in HRI of unstructured dialogic interaction would allow communication with robots that is as natural as communication with other humans. Turn-taking is the framework that provides structure for human communication. Consciously or subconsciously, humans are able to communicate their understanding and control of the turn structure to a conversation partner by using syntax, semantics, paralinguistic cues, eye gaze, and body language in a socially intelligent way. Our research aims to show that by implementing these turn-taking cues within a interaction architecture that is designed fundamentally for turn-taking, a robot becomes easier and more efficient for a human to interact with. This paper outlines our approach and initial pilot study into this line of research.

This paper describes results from a large-scale, complex human study using non-facial and non-verbal affect for victim management in robot-assisted Urban Search and Rescue Applications. Statistically significant results are presented that indicate participants felt emotive robots were more calming, friendlier, and attentive.

In this paper we present a model of language contextualization for spatially situated dialogue systems including service robots. The contextualization model addresses the problem of location sensitivity in language understanding for human-robot interaction. Our model is based on the application of situation-sensitive contextualization functions to a dialogue move's semantic roles — both for the resolution of specified content and the augmentation of empty roles in cases of ellipsis. Unlike the previous use of default values, this methodology provides a context-dependent discourse process which reduces unnecessary artificial clarificatory statements. We detail this model and report on a number of user studies conducted with a simulated robotic system based on this model.

We summarize our past, present and future research related to human-robot dialogue, starting with its foundations in collaborative discourse theory, continuing to our current research on recognizing and generating engagement, and concluding with an outline of new work we are beginning on the modeling of long-term relationships between humans and robots.

We propose a framework for a robot to participate in and activate multiparty conversation. In multiparty conversation, the robot should select its behavior based on both linguistic information and participation structure. In this paper, we focus on multiparty conversation game "Nandoku," which is often played in elderly care facilities. The robot acts as one of the participants, and tries to promote the communication activeness. The framework handles the dialogue situation from three aspects: multiparty conversation, game progress and communication activation, and selects the most effective robot's behavior according to these three aspects.