Max Robotics — Global Service Robot Directory + US Certification

About

Olga Napolitano is an emerging robotics and control systems researcher whose work sits at the intersection of autonomous robot navigation, active sensing, and model-based control. Her research focuses on developing intelligent perception-aware strategies that enable robots to maximize the quality of information they collect while performing complex tasks — a critical challenge in real-world robotic deployments where sensor noise and intermittent measurements can significantly degrade performance. Her most influential contribution, "Information-Aware Lyapunov-Based MPC in a Feedback-Feedforward Control Strategy for Autonomous Robots" (2022, 12 citations), introduced an elegant hybrid control architecture that couples active sensing with provably stable Lyapunov-based model predictive control. This work was complemented by her Gramian-based optimal sensing framework (2021, 8 citations), which formally addresses the trade-off between information maximization and noise minimization during robot motion. More recently, Napolitano has extended these ideas into machine learning contexts, proposing methods to improve training data quality for learned robot models through principled active sensing. Her 2025 work on risk-aware hierarchical navigation in intralogistics environments signals a growing interest in safety-critical real-world deployment. Though early in her career, her research offers meaningful contributions to making autonomous robots smarter, safer, and more perceptually efficient.