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.

Mobile robots are an increasingly important part of search and rescue efforts as well as military combat. 
In order for users to accept these robots and use them effectively, the user must be able to communicate clearly with the robots and obtain explanations of the robots' behavior that will allow the user to understand its actions. 
This paper describes part of a system of software that will be able to produce explanations of the robots' behavior and situation in an interaction with a human operator.

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.

Future service robots applications in healthcare may require systems to be adaptable in terms of verbal and non-verbal behaviors to ensure patient perceptions of quality healthcare. Adaptation of robot behaviors should account for patient emotional states. Related to this, there is a need for a reliable method by which to classify patient emotions in real-time during patient-robot interaction (PRI). Accurate emotion classification could facilitate appropriate robot adaptation and effective healthcare operations (e.g., medicine delivery). We conducted and compared two simulated robot medicine delivery experiments with different participant age groups and robot configurations. A meta-analysis of the data from these experiments was to identify a robust approach for emotional state classification across age groups and robot configurations. Results revealed age differences as well as multiple robot humanoid feature manipulations to cause inaccuracy in emotion classification using statistical and machine learning methods. Younger adults tend to have higher emotional variability than elderly. Combinations of robot features were also found to induce emotional uncertainty and extreme responses. These findings were largely reflected in terms of physiological responses rather than subjective reports of emotions.

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.

As robots become incorporated into our environments, they must be equipped with the ability to communicate effectively with us. In particular, robots that perform longer tasks for a small set of people (e.g., a companion robot to escort visitors to meetings all day) need to be able to start and maintain interesting and relevant dialog with any and all humans involved.In this work, we present our ongoing work on our robot, CoBot, which is assigned an all-day task to escort a visitor around our building and perform tasks for her. We first describe CoBot's dialog manager which is responsible for the task-oriented dialog, including dialog to meet the visitor's needs, CoBot's notifications of interesting locations around the building, and the robot's own requests for help. We, then, focus two aspects of the dialog manager: 1) how CoBot can invoke more accurate answers to its requests for help from the visitor and 2) how to reduce repetitive dialog which can happen during all-day interactions. We provide an example dialog between CoBot and a visitor to illustrate the dialog manager's capabilities.

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.

This paper summarizes our ongoing project on developing an architecture for a robot that can acquire new words and their meanings while engaging in multi-domain dialogues. These two functions are crucial in making conversational service robots work in real tasks in the real world. Household robots and office robots need to be able to work in multiple task domains and they also need to engage in dialogues in multiple domains corresponding to those task domains. Lexical acquisition is necessary because speech understanding cannot be done without enough knowledge on words that are possibly spoken in the task domain. Our architecture is based on a multi-expert model in which multiple domain experts are employed and one of them is selected based on the user utterance and the situation to engage in the control of the dialogue and physical behaviors. We incorporate experts that have an ability to acquire new lexical entries and their meanings grounded on the physical world through spoken interactions. By appropriately selecting those experts, lexical acquisition in multi-domain dialogues becomes possible. An example robotic system based on this architecture that can acquire object names and location names demonstrates the viability of the architecture.

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.

We are motivated by building a system for an autonomous robot companion that collaborates with a human partner for achieving a common mission. The objective of the robot is to infer the human's preferences upon the tasks of the mission so as to collaborate with the human by achieving human's non-favorite tasks. Inspired by recent researches about the recognition of human's intention, we propose a unified model that allows the robot to switch accurately between verbal and non-verbal interactions. Our system unifies an epistemic partially observable Markov decision process (POMDP) that is a human-robot spoken dialog system aiming at disambiguating the human's preferences and an intuitive human-robot collaboration consisting in inferring human's intention based on the observed human actions. The beliefs over human's preferences computed during the dialog are then reinforced in the course of the task execution by the intuitive interaction. Our unified model helps the robot inferring the human's preferences and deciding which tasks to perform to effectively satisfy these preferences. The robot is also able to adjust its plan rapidly in case of sudden changes in the human's preferences and to switch between both kind of interactions. Experimental results on a scenario inspired from robocup@home outline various specific behaviors of the robot during the collaborative mission.