Rosemary Emery-Montemerlo

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Partially observable decentralized decision making in robot teams is fundamentally different from decision making in fully observable problems. Team members cannot simply apply single-agent solution techniques in parallel. Instead, we must turn to game theoretic frameworks to correctly model the problem. While partially observable stochastic games (POSGs)(More)
In the real world, noisy sensors and limited communication make it difficult for robot teams to coordinate in tightly coupled tasks. Team members cannot simply apply single-robot solution techniques for partially observable problems in parallel because they do not take into account the recursive effect that reasoning about the beliefs of others has on(More)
This paper presents a real-time algorithm for acquiring compact three-dimensional maps of indoor environments, using a mobile robot equipped with range and imaging sensors. Building on previous work on real-time pose estimation during mapping, our approach extends the popular expectation-maximization algorithm to multisurface models, and makes it amenable(More)
In the field of multi-agent systems, researchers seek to understand and specify the interactions between agents as they cooperate to perform tasks in the real world. While success has been achieved in complex systems using hand-crafted rules, our ultimate goal is to design agents that perform optimally even if the system dynamics are not fully understood.(More)
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