Papers
21
Total Citations
185
H-Index
8
About
Ian Abraham is a robotics and control systems researcher whose work spans autonomous exploration, motion planning, and learning-based control. He is perhaps best known for his pioneering contributions to **ergodic search and exploration**, a mathematically principled framework that enables robots to intelligently allocate search effort proportional to the likelihood of finding targets — work that has accumulated significant citations and been extended across multiple domains including safety-critical environments, multi-agent systems, and time-optimal planning. His research on **hybrid reinforcement learning** bridges model-based and model-free approaches, improving how robotic systems learn from limited experience. Abraham has also made meaningful contributions to **legged locomotion**, from nonlinear damping models for predicting running dynamics to demonstrating that linear policies suffice for low-cost quadrupedal robots navigating rough terrain. His use of the **Koopman operator** for active learning reflects a broader interest in data-driven methods for control. Across his portfolio — totaling over 150 citations — Abraham consistently addresses real-world challenges such as search and rescue, tactile sensing, and safe navigation in cluttered environments, making his work both theoretically rigorous and practically impactful for the broader robotics community.
Research Focus
Key Achievements
Top Papers
- 1Ergodic Exploration Using Binary Sensing for Nonparametric Shape Estimation30 citations · 2017
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- 4Time Optimal Ergodic Search16 citations · 2023
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- 8A Local Optimization Framework for Multi-Objective Ergodic Search8 citations · 2022
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