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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)
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)
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)
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)
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)
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)
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)