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NIFTi is about human-robot cooperation. About teams of robots and humans doing tasks together, interacting together to try and reach a shared goal. NIFTi looks at how the robot could bear the human in mind. Literally. When determining what to do or say next in human-robot interaction; when, and how. NIFTi puts the human factor into cognitive robots, and human-robot team interaction in particular.   Each year, NIFTi evaluates its systems together with several USAR organizations. Rescue personnel teams up with NIFTi robots to carry out realistic missions, in real-life training areas. 

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DR 4.3.2: Theory and evaluation of working agreement method and HRI-adaptation to di erent contexts

This report presents the results of WP4 for the third year of NIFTi. The overall objective of WP4 is to improve joint exploration by decreasing the cognitive task load for the human workers and optimizing the robots operational deployment. This objective will result in core UI design and evaluation activities aiming at theoretical and empirical founded solutions to support human's situation awareness and joint human-robot performance. This support instantiates working agreements for shared situation awareness and task load allocation, which should be adaptable to the current operational context. In year 3, the work in WP4 focused on four core functions of such adaptive support. For the first function, real-time operator task load assessment, the load model was refined, parameterized, implemented and tested. The second function concerns the setting of working agreements for robot's level of team-membership. A first experiment provided requirements for the communication and task load that should drive the actual setting of robot autonomy. The third function centers on adapting the tactical display to the momentary user needs and context. An agent-based architecture was developed that enables real-time decisions of (adaptive) information presentation (i.e., to establish context-sensitive "Right Messages at the Right Moment in the Right Modality" (called situated (RM) 3). A first implementation was tested in the end-user evaluation. The fourth function focuses on selectional attention, the modeling of visual search and task-switching. This WP provided preliminary computational models for (a) attention in real-world scenarios and (b) shifting and inhibition controls.

m32-DR4.3.2-PUBLIC.pdf — PDF document, 4619Kb

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