Information Gathering Actions Over Human Internal State
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(2016)
摘要
Much of estimation of human internal state (goal, intentions, activities, preferences, etc.) is passive: an algorithm observes human actions and updates its estimate of human state. In this work, we embrace the fact that robot actions affect what humans do, and leverage it to improve state estimation. We enable robots to do active information gathering, by planning actions that probe the user in order to clarify their internal state. For instance, an autonomous car will plan to nudge into a human driver's lane to test their driving style. Results in simulation and in a user study suggest that active information gathering significantly outperforms passive state estimation.
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关键词
human internal state estimation,human action observation,robot action,active information gathering,action planning,autonomous car,human driver lane,driving style,passive state estimation
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