Adaptive Control and Robotics (ARC) Lab
Omkar Sudhir Patil
The ARC Lab develops mathematically certified learning and control methods.
Lab Highlights
- Learning-based control with stability and performance guarantees.
- Online adaptation for uncertain nonlinear systems.
- Safety-critical autonomy under uncertainty.
- Structure-aware learning for dynamical and robotic systems.
- Theory-to-hardware validation on autonomous platforms.
The Adaptive Control and Robotics (ARC) Lab develops theoretically grounded methods for learning, control, and autonomy in nonlinear dynamical systems. Our research combines nonlinear and adaptive control, machine learning, optimization, and robotics, with particular emphasis on methods that provide rigorous guarantees of stability, safety, robustness, and performance. We study problems including Lyapunov-based learning and adaptive control, safety-critical autonomy, multi-agent coordination, and learning-enabled robotic systems, with applications spanning ground, aerial, and manipulator platforms.