Tejas R. Mehta

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In this paper, we present a multi-pronged approach to the " Learning from Example " problem. In particular, we present a framework for integrating learning into a standard, hybrid navigation strategy, composed of both plan-based and reactive controllers. Based on the classification of colors and textures as either good or bad, a global map is populated with(More)
— Multi-modal control is a commonly used design tool for breaking up complex control tasks into sequences of simpler tasks. It has previously been shown that rapidly-exploring randomized trees (as well as other viable approaches) can be used for reachability computations given a set of modes, and reinforcement learning can be performed over the reachable(More)
— In this paper, we study hybrid models that not only undergo mode transitions, but also experience changes in dimensions of the state and input spaces. An algorithmic framework for the optimal control of such Multi-Mode, Multi-Dimension (or M 3 D) systems is presented. We moreover derive a detailed M 3 D model for an ice-skater, and demonstrate the use of(More)
To see a world in a grain of sand, And a heaven in a wild flower, ... ACKNOWLEDGMENTS First and foremost, I would like to express my appreciation to my advisor, Dr. Magnus Egerstedt, for his guidance and support, without which this dissertation would not have materialized. Your unquenchable enthusiasm and tireless hardwork have been the most invaluable(More)
ACKNOWLEDGEMENTS I wish to express my sincere appreciation to my thesis advisor, Professor Magnus Egerstedt, for his guidance, patience, and support. I am also specially grateful to Professor Edward W. Kamen for his support and encouragement early on in my PhD studies, as my first advisor. I also wish to thank Professors Erik I. Verriest and Yorai Wardi for(More)