Anil Aswani
Papers
3
Total Citations
185
H-Index
3
About
Anil Aswani’s research lies at the intersection of control theory, machine learning, and robotics, with a focus on creating safe, data-driven systems that can learn and adapt in real time. He is best known for pioneering **learning-based model predictive control (LBMPC)** , a framework that rigorously integrates statistical learning with control engineering while preserving guarantees on safety, robustness, and convergence. His landmark 2012 paper on the onboard implementation of LBMPC on a quadrotor helicopter (174 citations) demonstrated how such algorithms can operate under real-time constraints, bridging theory and practice in autonomous systems. Aswani has also advanced **robot imitation learning**, notably through work on dynamic regret convergence and adaptive regularization for on-policy methods like DAgger (2021), addressing fundamental questions about how robots can efficiently learn from human supervisors without catastrophic performance swings. Beyond these contributions, his comparative studies of optimization algorithms for learning-based MPC (2014) provide practical guidance for deploying these methods in robotics and automation. With a career marked by rigorous theoretical foundations and impactful experimental validation, Aswani’s work continues to shape how autonomous systems learn from data while maintaining the safety and reliability essential for real-world deployment.
Research Focus
Key Achievements
Top Papers
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