Abstract—There is considerable interest in real-world formation-maintenance tasks, where robots move together while maintaining a geometric shape. This interest is motivated by promise of robustly and efficiently moving multiple robots along a path, guided by a human operator. This paper presents a comprehensive set of techniques that fulfill this promise: (i) a novel method for fusing open- and closed- loop controllers, for robust formation-maintenance; (ii) an ecological display, allowing a human operator to monitor and guide robots, while improving their performance and reducing the failure rate; and (iii) a set of methods for interacting with the formation in the case of a disconnect in the formation. We evaluate each of these contributions in extensive experiments, including 25 human operators. We show significant improvements in performance (in terms of movement time), robustness (both in number of failures, as well as failure rate), and consistency between operators. I.

Teaching is inherently a social interaction between teacher and student. Despite this knowledge, many educational tools, such as vocabulary training programs, still model the interaction in a tutoring scenario as unidirectional knowledge transfer rather than a social dialog. Therefore, ongoing research aims to develop virtual agents as more appropriate media in education. Virtual agents can induce the perception of a life-like social interaction partner that communicates through natural modalities such as speech, gestures and emotional expressions. This effect can be additionally enhanced with a physical robotic embodiment. This paper presents the development of social supportive behaviors for a robotic tutor to be used in a language learning application. The effect of these behaviors on the learning performance of students was evaluated. The results support that employing social supportive behavior increases learning efficiency of students.

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Within the context of human/multi-robot teams, the “help me help you ” paradigm offers different opportunities. A team of robots can help a human operator accomplish a goal, and ahumanoperatorcanhelpateamofrobotsaccomplishthe same, or a different, goal. Two scenarios are examined here. First, a team of robots helps a human operator search a remote facility by recognizing objects of interest. Second, the human operator helps the robots improve their position (localization) information by providing quality control feedback.

This extended abstract briefly describes a new methodology for shared decision-making in human/multi-robot teams. We present a new application of FORR, acognitivearchitecture that considers the opinions of others when choosing actions. While robots in our system ultimately make their own decisions about their actions, they do so based on collective advice from humans, agents and other robots.

Using focal point learning to improve human–machine