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)
This paper presents a solution to the problem of constructing control programs, i.e. sequences of control modes, from a given motion alphabet. In particular, techniques are developed that enable reinforcement learning to act directly at the mode level, and hence make learning applicable to continuous time control systems in a straightforward manner.… (More)
In this article, we introduce the MODEbox tool for automatically producing hybrid, multimodal control programs from data. In particular, given an I/O string, four distinct operational units are introduced. The application of the MODEbox tool is illustrated on two different examples involving robots and ants.
— 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)
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)
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)