Sam Moore

PhD Candidate, ME
ML Scientist, Roboticist, Dynamicist
Duke University
sam.a.moore@duke.edu

I am an NSF Graduate Research Fellow in the General Robotics Lab @ Duke University, advised by Dr. Boyuan Chen, and Dr. Brian Mann.

As a PhD student, I have worked across machine learning, robotics, dynamical systems, and control.

Prior to Duke, I completed my bachelors studies at the University of Kentucky in kinesiology and statistics.

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My research interests center on integrating dynamics, control, and machine learning to push the boundaries of robotics and, where applicable, tackle challenges across a range of scientific disciplines.

This paper introduces an approach to milling stability analysis whereby the vibration behavior of a milling tool during cutting is used to directly obtain the parameters of a delay differential equation model of milling.

This work introduces a model-free sampling method for basins of attraction. The proposed method is based upon hybrid active learning and is designed to find and label the “informative” samples, which efficiently determine the boundary of basins of attraction.

This paper seeks to explore and compare supervised learning methods for phase identification (i.e., roll, slip, and hop) in simulated data from a driven eccentric wheel as a prototypical non-smooth system.

An eccentric disk has a non-constant normal force and therefore has four distinct phases of motion: oscillations about a stable equilibrium, roll without slip, roll with slip, and hop. In this work, the system is analytically modeled using an augmented Lagrangian formulation, solved with numerical integration, and experimentally realized.

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