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Ahalya PrabhakarPhD StudentAdviser: Todd Murphey

How can we enable robots to learn representations of the world that allow for robust performance and control? My work focuses on robot learning for flexible task representations from variable, imperfect human demonstrations and high-dimensional sensory learning using distribution-based methods and information-theoretic measures.

MS in Mechanical Engineering from Northwestern University, 2016

BS in Mechanical Engineering from California Institute of Technology, 2013

Abraham, I., A. Prabhakar, and T. D. Murphey, "Active Area Coverage from Equilibrium", Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018. Google Scholar

Abraham, I., A. Prabhakar, M. J. Z. Hartmann, and T. D. Murphey, "Ergodic Exploration using Binary Sensing for Non-Parametric Shape Estimation", IEEE Robotics and Automation Letters, vol. 2, issue 2, pp. 827-834, 2017. Google Scholar

A. Prabhakar, A. Mavrommati, J. A. Schultz, and T. D. Murphey, "Autonomous Visual Rendering using Physical Motion", Workshop on the Algorithmic Foundations in Robotics (WAFR) 2016, 2016.  Google Scholar

A. Prabhakar, K. Flaßkamp, and T. D. Murphey, "Symplectic Integration for Optimal Ergodic Control", IEEE Int. Conf. on Decision and Control (CDC) 2015, 2015. Google Scholar

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