معرفی
Ruggero Carli is an Associate Professor at the Department of Information Engineering, University of Padova. His research focuses on control systems, robotics, and optimization, with emphasis on model-based reinforcement learning, distributed optimization algorithms, and energy systems.
His work bridges theoretical advancements with real-world applications, including autonomous robotics, smart grids, and nonlinear control. Key contributions include physics-informed machine learning frameworks, ADMM-based distributed optimization methods, and MPC-driven control solutions for underactuated systems.
Research interests include:
- Model-Based Reinforcement Learning for Robotics
- Nonlinear Model Predictive Control (NMPC)
- Distributed Optimization and ADMM Variants
- Energy Networks and Smart Grids
- Robot Dynamics and System Identification
Recent publications emphasize:
- Continual learning for driver behavior analysis
- Physics-informed control for underactuated systems
- Robust optimization in unreliable networks
- Autonomous robotic manipulation with large language models
His research integrates control theory with modern machine learning techniques, addressing challenges in edge computing, distributed systems, and real-time implementation.
