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.

As part of the "Survivor Buddy" project, we have created an open source speech translator toolkit which allows written or spoken word from multiple independent controllers to be translated into either a single synthetic voice, synthetic voices for each controller, orunchanged natural voice of each controller. The human controllers can work via the internet or be physically co-located with the Survivor Buddy robot. The toolkit is expected to be of use for exploring voice in general human-robot interaction. The Survivor Buddy project is motivated by our prior work which suggests that a trapped victim of a disaster, or other human who is dependent, will treat a rescue robot as a social medium and that the choice of robotic voice will be important. The robot will be both a medium to the "outside" world and a local, independent entity devoted to the victim Figure 1: View from the Survivor Buddy webcam with subpicture (e.g., a buddy).

Robots are being considered for applications where they serve as proxies for humans interacting with another human,such as emergency response, hostage negotiation, and healthcare. In these domains, the human (“dependent”) is connected to multiple other humans (“controllers”) via the robot proxy for long periods of time. The dependent may want to interact with humans but also to engage the robot as a medium to the World Wide Web. In the future, medical personnel may use the robot for victim assistance and comfort while the rescue team plans and monitors extrication. Other applications include healthcare, where the robot is the link between a patient and a medical provider for intermittent,routine interactions, and hostage negotiation, where police may use a bomb squad robot to talk with and build rapport with the suspect while the SWAT team uses the robot’s sensors to build and maintain situation awareness.Under funding from the National Science Foundation, we are finishing the first year of investigating verbal and nonverbal communication strategies for robots who are serving as proxies for multiple humans interact with the humans who are dependent on them. Our work posits that such a robot would occupy a novel social medium position according to the Computers as Social Actors (CASA) model [Nass,Steuer, and Tauber1994] [Reeves and Nass1996]. Given that teleoperated robots are treated socially, it is unlikely that a rescue robot would be treated as a pure medium even if playing music or videos. Likewise, the limitations of autonomy and the interactions of specialists with the dependent prevent the robot from being a true social actor. Instead, social actor and pure medium are two extremes on the agent identity spectrum, with a social medium occupying a middle position.A social medium would be perceived as a loyal, helpful “go between” who is an advocate for the dependent, rather than a device for accomplishing the goals of multiple controllers(medical specialist, structural engineer, rescue operations official, etc.). To explore the social medium identity,we have built a physical prototype of a Survivor Buddy and are creating autonomous affective behaviors and a social medium toolkit to explore human-robot interaction.

Our goal is to develop an interface for older people to effectively communicate with a robotic assistant so that they can safely remain living in their home. We are devising a multimodal interface since people communicate with one another using a variety of verbal and non-verbal signals, including haptics, i.e., physical interactions. We view haptics as an integral component of communication, which in some cases drives the interaction between the user and the robot, and we study its relation to speech and gestures. We illustrate features of interactions between an elderly person and an assistant via excerpts from our ongoing data collection. We also describe the architecture of our interface and ongoing research to bring this interface to fruition.

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.