Paulina Varshavskaya

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We have built a biologically and neurally inspired autonomous mobile robotic worm. The main aim of the project is to demonstrate elegant motion on a robot with a large number of degrees of freedom (DOFs) under the control of a simple distributed neural system as found in many animals’ spinal cord. Our robot consists of individually controlled segments that(More)
Circulating tumor cells (CTCs) are malignant cells that have migrated from solid cancers into the blood, where they are typically present in rare numbers. There is great interest in using CTCs to monitor response to therapies, to identify clinically actionable biomarkers, and to provide a non-invasive window on the molecular state of a tumor. Here we(More)
The past decade has seen an explosion of research in humanoid robotics. The stated motivations for this work have varied widely. Many teams have concentrated on bipedal locomotion, some have been interested in human level social interactions, understanding human intelligence, modeling human learning capabilities and others have been more interested in(More)
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 on-line. 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)
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)
What: Recently there has been an important research effort into modular, distributed robotics and in particular, self-reconfiguring robotics [2, 5, 8]. Issues with designing controllers for such systems range from constructing motor control primitives to ensuring cooperation between modules. For simpler tasks, such as locomotion in one direction, hand(More)