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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 4.4.4: Summative evaluation and theory of the setting-up and usage of adaptive HRI

WP4 aims at enhanced effectiveness and efficiency of joint human-robot exploration by dynamic task load distribution among rescuers and robots, preventing overload and optimizing attention allocation. In year 4, we focused on the integration of the prototype components into an integrated, operational system. The cognitive task load model and the team awareness display were integrated into the NIFTi system, providing the (adaptive) working agreement policies, personalized views on the current (shared) knowledge-base (i.e. supporting situation and team awareness), and context- sensitive information exchanges (e.g., ”smart questions”). In addition, 3D eye-tracking experiments were conducted to further develop computational visual attention models aimed at solving top-down search tasks. We applied the methodology for situated Cognitive Engineering (sCE) during the 4 years of the NIFTi project to establish a requirements baseline with a sound and practical design rationale. Over the years, NIFTi-robots’ level of autonomy, user model, and team membership were enhanced for disaster management scenarios with increasing scale and complexity (from tunnel and train accident to earth quake). The current requirements baseline, with its design rationale, provides a sound basis for (1) implementing state-of-the-art collaborative rescue robots, and (2) further developing of such robots with higher levels of persistence. The last year, we extended the sCE-methodology by incorporating interaction design patterns that provide a structured format to capture and share design knowledge on the communication level (i.e. the shape of the interaction).

DR4.4.4.niftireport_PUBLIC.pdf — PDF document, 500Kb

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