Interactive & Emergent Autonomy Lab

Ian Abraham

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

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

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