Ian Abraham
Alumnus
Advisor: Todd Murphey
i-abr.github.io
iabr4073@gmail.com
Google Scholar
Research
How can autonomous systems understand and model the world through purposeful interaction and modeling of unknown dynamic processes? My research focuses on answering this question through the development of algorithms that combines active sensing and learning into optimal control for robotic systems.
Projects
Active Learning and Data-Driven Control
Cyber Physical Systems in Uncertain Environments
Haptic Languages
Vibrissal Responsive Neurons
Education
Ph.D. Mechanical Engineering, Northwestern University, 2020
M.S. Mechanical Engineering, Northwestern University, 2017
B.S. Mechanical Engineering, Minor in Mathematics, Rutgers University, 2014
Teaching
TA for Machine Dynamics (ME 314), Fall 2017
Publications
Ergodic Specifications for Flexible Swarm Control: From User Commands to Persistent Adaptation
A. Prabhakar, I. Abraham, A. Taylor, M. Schlafly, K. Popovic, G. Diniz, B. Teich, B. Simidchieva, S. Clark, T. D. Murphey
Robotics Science and Systems, 2020. PDF Video
Active learning of dynamics for data-driven control using Koopman operators
I. Abraham and T. D. Murphey
IEEE Transactions on Robotics, 2019. PDF, Code
Decentralized ergodic control: Distribution-driven sensing and Exploration for multi-agent systems
I. Abraham and T. Murphey
IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 2987–2994, 2018. PDF, Video
Real-time area coverage and target localization using receding-horizon ergodic exploration
A. Mavrommati, E. Tzorakoleftherakis, I. Abraham, and T. D. Murphey
IEEE Transactions on Robotics, vol. 34, no. 1, pp. 62–80, 2018. PDF, Video 1, Video 2
Active area coverage from equilibrium
I. Abraham, A. Prabhakar, and T. D. Murphey
Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018. PDF, Appendix, Video
Data-driven measurement models for active localization in sparse environments
I. Abraham, A. Mavrommati, and T. D. Murphey
Robotics: Science and Systems Proceedings, 2018. PDF, Video
Structured neural networks for model-based control
A. Broad, I. Abraham, B. Argall, and T. D. Murphey
Robotics: Science and Systems (RSS) Workshop on Learning and Inference in Robotics, 2018. PDF
Ergodic exploration using binary sensing for non-parametric shape estimation
I. Abraham, A. Prabhakar, M. Hartmann, and T. Murphey
IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 827–834, 2017. PDF, Video
Model-based control using Koopman operators
I. Abraham, G. de la Torre, and T. Murphey
Robotics: Science and Systems Proceedings, 2017. PDF, Code