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Richard M. Murray
Researcher at California Institute of Technology
Publications - 731
Citations - 74988
Richard M. Murray is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Control theory & Linear temporal logic. The author has an hindex of 97, co-authored 711 publications receiving 69016 citations. Previous affiliations of Richard M. Murray include University of California, San Francisco & University of Washington.
Papers
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Counter-example Guided Learning of Bounds on Environment Behavior.
TL;DR: This work presents a data-driven solution that allows for a system to be evaluated for specification conformance without an accurate model of the environment.
Proceedings ArticleDOI
Dynamic Sensor Coverage with Uncertainty Feedback : Analysis Using Iterated Maps.
TL;DR: In this paper, the authors consider a simple case of two spatially separate uncertain systems 1 and 2 and present an analysis of the dynamic sensor coverage problem with uncertainty feedback, where the sensor decides to measure system 1 or 2 based on the relative uncertainty of its estimates of the states of the two systems.
Posted ContentDOI
Model Reduction Tools For Phenomenological Modeling of Input-Controlled Biological Circuits
Ayush Pandey,Richard M. Murray +1 more
TL;DR: This model reduction approach combines the common assumptions of time-scale separation, conservation laws, and species’ abundance to obtain the reduced models that can be used for design of synthetic biological circuits.
Proceedings ArticleDOI
Temporal logic planning in uncertain environments with probabilistic roadmaps and belief spaces
TL;DR: This paper shows that point-based value iteration can be combined with probabilistic roadmaps to solve this planning problem over the belief space of the uncertain environment.
Journal ArticleDOI
Performance of Non-Homogeneous Multi-Agent Systems on a Graph
TL;DR: The sensitivity transfer functions between every pair of agents are derived and performance of non-homogeneous systems is analyzed, showing that the low frequency behavior is influenced not only by topology, but also by static gain and poles of the agents.