Papers
12
Total Citations
623
H-Index
8
About
Claudio Gaz is a leading researcher in robot dynamics, identification, and control, with a particular focus on extracting feasible dynamic parameters for robotic manipulators. His most influential work, the 2019 paper "Dynamic Identification of the Franka Emika Panda Robot With Retrieval of Feasible Parameters Using Penalty-Based Optimization," has garnered over 310 citations, establishing a benchmark for accurate robot modeling. Gaz pioneered methods to retrieve physically consistent parameters from linear dynamic coefficients, enabling realistic simulations and precise control—a critical advancement for collaborative and industrial robots. His reverse engineering of the KUKA LWR’s dynamic model (81 citations) provided the research community with essential, previously proprietary data. Beyond identification, Gaz has made significant contributions to human-robot collaboration, collision detection, and payload estimation, as well as innovative work in robot-assisted medical procedures, including online needle-tissue interaction modeling for enhanced force feedback in teleoperated surgery. His research also spans underactuated robot control and redundancy resolution, demonstrating a broad impact on both theoretical foundations and practical applications. With over 600 total citations, Claudio Gaz continues to shape the field of robotics through rigorous, application-driven research.
Research Focus
Key Achievements
Top Papers
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- 9An Online Learning Procedure for Feedback Linearization Control without Torque Measurements8 citations · 2019
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