Human-robot collaborative tutoring using multiparty multimodal spoken dialogue

In this paper, we describe a project that explores a novel experi-mental setup towards building a spoken, multi-modally rich, and human-like multiparty tutoring robot. A human-robot interaction setup is designed, and a human-human dialogue corpus is collect-ed. The corpus targets the development of a dialogue system platform to study verbal and nonverbal tutoring strategies in mul-tiparty spoken interactions with robots which are capable of spo-ken dialogue. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. Along with the participants sits a tutor (robot) that helps the par-ticipants perform the task, and organizes and balances their inter-action. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies, such as a microphone array, Kinects, and video cameras, were coupled with manual annotations. These are used build a situated model of the interaction based on the participants personalities, their state of attention, their conversational engagement and verbal domi-nance, and how that is correlated with the verbal and visual feed-back, turn-management, and conversation regulatory actions gen-erated by the tutor. Driven by the analysis of the corpus, we will show also the detailed design methodologies for an affective, and multimodally rich dialogue system that allows the robot to meas-ure incrementally the attention states, and the dominance for each participant, allowing the robot head Furhat to maintain a well-coordinated, balanced, and engaging conversation, that attempts to maximize the agreement and the contribution to solve the task. This project sets the first steps to explore the potential of us-ing multimodal dialogue systems to build interactive robots that can serve in educational, team building, and collaborative task solving applications.