Ian Abraham

Assistant Professor of Mechanical Engineering & Materials Science
Office Address:
17 Hillhouse Avenue
New Haven, CT 06511
Mailing Address:
P.O. Box 208284
New Haven, CT 06520
Email: ian.abraham@yale.edu
  • Ph.D., Northwestern University
  • M.S., Northwestern University
  • B.S., Rutgers University


My research interest lies at the intersection of robotics, optimal control, machine learning, and artificial intelligence with a focus on active sensing and learning.

Robotic systems with the ability to independently gather information needed for solving arbitrary tasks are critical for interacting in uncertain and unstructured environments. My group researches these robotics systems through algorithmic developments that tightly integrate theory and applied research, enabling robots to optimally learn, explore, and dynamically interact with the environment and other robots. Our work spans a wide range of problems in optimal control, exploration, sample-efficient learning, reactive and hybrid control, optimization, locomotion, and multi-agent systems. Ultimately, the goal of my research is to enhance robotic systems to be self-sufficient and adaptive in unstructured environments, leveraging collaboration with other robots with minimal human intervention.

Selected Awards & Honors:

  • Northwestern University Belytschko Outstanding Research Award 2020
  • IEEE King-Sun Fu T-RO Best Paper Award 2019

Selected Publications:

     For a complete list of publications please see Google Scholar.


  • Abraham, Ian, Ahalya Prabhakar, and Todd D. Murphey. "An ergodic measure for active learning from equilibrium." IEEE Transactions on Automation Science and Engineering (2021).
  • Abraham, Ian, et al. "Model-based generalization under parameter uncertainty using path integral control." IEEE Robotics and Automation Letters 5.2 (2020): 2864-2871.
  • Abraham, Ian, et al. "Hybrid control for learning motor skills." Algorithmic Foundations of Robotics XIV, 2021.
  • Abraham, Ian, and Todd D. Murphey. "Active learning of dynamics for data-driven control using Koopman operators." IEEE Transactions on Robotics 35.5 (2019): 1071-1083.
  • Abraham, Ian, and Todd D. Murphey. "Decentralized ergodic control: distribution-driven sensing and exploration for multiagent systems." IEEE Robotics and Automation Letters 3.4 (2018): 2987-2994.
  • Abraham, Ian, Anastasia Mavrommati, and Todd D. Murphey. "Data-driven measurement models for active localization in sparse environments." Robotics: Science and Systems (2018).
  • Abraham, Ian, et al. "Ergodic exploration using binary sensing for nonparametric shape estimation." IEEE robotics and automation letters 2.2 (2017): 827-834.
  • Abraham, Ian, Gerardo De La Torre, and Todd D. Murphey. "Model-based control using Koopman operators." Robotics: Science and Systems (2017).