Data clustering is a common technique for statistical data analysis. It is used in many fields including machine learning, data mining, customer segmentation, trend analysis, pattern recognition and image analysis. The proposed Localized Diffusion Folders methodology performs hierarchical clustering of high-dimensional datasets. The diffusion folders are multi-level data partitioning into local neighborhoods that are generated by several random selections of data points and folders in a diffusion graph and by defining local diffusion distances between them. This multi-level partitioning defines an improved localized geometry of the data and a localized Markov transition matrix that is used for the next time step in the diffusion process. The result of this clustering method is a bottom-up hierarchical clustering of the data while each level in the hierarchy contains localized diffusion folders of folders from the lower levels. This methodology preserves the local neighborhood of each point while eliminating noisy connections between distinct points and areas in the graph. The performance of the algorithms is demonstrated on real data and it is compared to existing methods.

TeamTalk is dialogue framework that supports multi-participant spoken interaction between humans and robots in a task-oriented setting that requires cooperation and coordination between team members. This paper describes some recently added features to the system, in particular the ability for robots to accept and remember location labels and the ability to learn action sequences. These capabilities reflect the incorporation into the system of an ontology and an instruction understanding component.

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.

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.

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.

This paper summarizes the online machine learning method LCore, which enables robots to learn to communicate with users from scratch through verbal and behavioral interaction in the physical world. LCore combines speech, visual, and tactile information obtained through the interaction, and enables robots to learn beliefs regarding speech units, words, the concepts of objects, motions, grammar, and pragmatic and communicative capabilities. The overall belief system is represented by a dynamic graphical model in an integrated way. Experimental results show that through a small, practical number of learning episodes with a user, the robot was eventually able to understand even fragmental and ambiguous utterances, respond to them with confirmation questions and/or actions, generate directive utterances, and answer questions, appropriately for the given situation. This paper discusses the importance of a developmental approach to realize personally and physically situated human-robot conversations.

In this paper, we describe an evaluation of the impact of embodiment, the effect of different kinds of embodiment, and the benefits of different aspects of embodiment, on direction-giving systems. We compared a robot, embodied conversational agent (ECA), and GPS giving directions, when these systems used speaker-perspective gestures, listener-perspective gestures and no gestures. Results demonstrated that, while there was no difference in direction-giving performance between the robot and the ECA, and little difference in participants’perceptions, there was a considerable effect of the type of gesture employed, and several interesting interactions between type of embodiment and aspects of embodiment.

This paper aims at bringing social robots closer to naive users. A Natural Programming System that allows the end-user to give instructions to a Social Robot has been developed. The instructions derive in a sequence of actions and conditions, that can be executed while the own sequence verbal edition continues. A Dialogue Manager System (DMS) has been developed in a Social Robot. The dialog is described in a voiceXML structure, where a set of information slots is defined. These slots are related to the necessary attributes for the construction of the sequence in execution time. The robot can make specific requests on encountering unfilled slots. Temporal aspects of dialog such as barge-in property, mixed-initiative, or speech intonation control are also considered. Dialog flow is based on Dialog Acts. The dialog specification has also been extended for multimodality management. The presented DMS has been used as a part of a Natural Programming System but can also be used for other multimodal humanrobot interactive skills.

We outline several challenges and opportunities for building physically situated systems that can interact in open, dynamic, and relatively unconstrained environments. We review a platform and recent progress on developing computational methods for situated, multiparty, open-world dialog, and highlight the value of representations of the physical surroundings and of harnessing the broader situational context when managing communicative processes such as engagement, turn taking, language understanding, and dialog management. Finally, we outline an open-world learning challenge that spans these different levels