Paulina Varshavskaya

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Designing distributed controllers for self-reconfiguring modular robots has been consistently challenging. We have developed a reinforcement learning approach which can be used both to automate controller design and to adapt robot behavior online. In this paper, we report on our study of reinforcement learning in the domain of self-reconfigurable modular(More)
— We propose a novel modular underwater robot which can self-reconfigure by stacking and unstacking its component modules. Applications for this robot include underwater monitoring, exploration, and surveillance. Our current prototype is a single module which contains several subsystems that later will be segregated into different modules. This robot(More)
— We propose to automate controller design for distributed modular robots. In this paper, we present some initial experiments with learning distributed controllers for synthesizing compliant locomotion gaits for modular, self-reconfigurable robots. We use both centralized and distributed policy search and find that the learning approach is promising, as(More)
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