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)
Multi-modal control is a commonly used design tool for breaking up complex control tasks into sequences of simpler tasks. In this paper , we show that by viewing the control space as a set of such tokenized instructions rather than as real-valued signals, reinforcement learning becomes applicable to continuous-time control systems. In fact, we show how a(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)
for dedicating their life to me and always supporting me in all my endeavors ... ... and to my love, Maya, for being my constant source of inspiration and encouragement. ACKNOWLEDGMENTS This thesis is a culmination of my academic career at the Georgia Institute of Technology. I have been fortunate to have excellent teachers and mentors throughout my entire(More)
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