Gianluca Cesari

Union Bank of Switzerland, École Polytechnique Fédérale de Lausanne

Papers

Total Citations

223

H-Index

About

Gianluca Cesari is a leading roboticist whose work bridges the gap between autonomous navigation and physical human-robot interaction. His primary research focuses on developing intelligent motion planning algorithms for robots operating in dynamic, human-populated environments, alongside advancing force estimation techniques for safer manipulation. Cesari’s most impactful contribution is his 2020 paper on robot navigation in crowded spaces, which has garnered 128 citations. By fusing imitation learning with deep reinforcement learning, he created a framework that enables mobile robots to move in a socially compliant and safe manner among pedestrians—a critical step for deploying service robots in malls, hospitals, and airports. Complementing this, his highly cited 2015 work (95 citations) introduced a novel method for estimating Cartesian contact forces using Kalman filters and generalized momentum. This approach allows robotic manipulators to detect collisions and perform force-controlled tasks without expensive external sensors, making industrial and collaborative robots more accessible and safer. Cesari’s dual focus on high-level navigation and low-level sensing demonstrates a comprehensive vision for robots that can both understand their social context and physically interact with the world.