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)
Self-reconfiguring modular robots have been receiving great attention because advances in our field are expected to deliver ultra-adaptable and robust systems. There has been remarkable progress in modular hardware and distributed controllers, e.g., [1]–[4], some of which were designed automatically by genetic algorithms, e.g., [1]. But how can the greatest(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)
Distributed robotic systems can benefit from automatic controller design and online adaptation by reinforcement learning (RL), but often suffer from the limitations of partial observability. In this paper, we address the twin problems of limited local experience and locally observed but not necessarily telling reward signals encountered in such systems. We(More)
In this thesis, we study distributed reinforcement learning in the context of automating the design of decentralized control for groups of cooperating, coupled robots. Specifically, we develop a framework and algorithms for automatically generating distributed controllers for self-reconfiguring modular robots using reinforcement learning. The promise of(More)
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