Andrea Carron
ETH Zurich, Institute for Biomedical Engineering, University of Padua
Papers
13
Total Citations
403
H-Index
8
About
Andrea Carron is a robotics and control researcher whose work spans data-driven control, multi-agent systems, and autonomous robotics. Best known for his contributions to Model Predictive Control (MPC) and machine learning-based approaches, Carron has helped bridge the gap between theoretical control frameworks and practical robotic deployment. His 2019 paper "Data-Driven Model Predictive Control for Trajectory Tracking With a Robotic Arm," now with 235 citations, demonstrated how learning-based MPC can enable high-precision manipulation in compliant, cost-effective robots — a significant advancement over traditional stiffness-reliant industrial systems. Extending this work to mobile manipulation, his Bayesian Multi-Task Learning MPC framework (2023, 45 citations) addresses the challenge of robots generalizing across diverse real-world tasks. Carron has also made notable contributions to cooperative multi-robot systems, including distributed localization, coverage control, and connectivity-constrained MPC. More recently, his work on autonomous racing — including the ForzaETH Race Stack and data-driven overtaking planners — has pushed the boundaries of real-time decision-making on commercial hardware. Through open-source platforms like Chronos and CRS, he has further demonstrated a commitment to making robotics research accessible and reproducible for both educational and research communities.
Research Focus
Key Achievements
Top Papers
- 1Data-Driven Model Predictive Control for Trajectory Tracking With a Robotic Arm235 citations · 2019
- 2Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulation45 citations · 2023
- 3
- 4Model Predictive Coverage Control20 citations · 2020
- 5
- 6
- 7
- 8
- 9
- 10