Papers
6
Total Citations
171
H-Index
5
About
C. Presse has made foundational contributions to the field of robot dynamics and control, with a primary focus on the identification of inertial and drive gain parameters. Their research centers on developing rigorous mathematical frameworks—including minimal linear energy models, least squares techniques, and Bayesian approaches—to accurately estimate a robot's dynamic properties from motion data. A key achievement is the introduction of "exciting trajectories" criteria (2002, 123 citations), which ensures that experimental data sufficiently excites all relevant parameters for reliable identification. Presse also pioneered the use of total least squares and sequential identification methods to handle noisy measurements and modeling errors, significantly improving robustness in real-world robotic systems. Their Bayesian estimation work (2003) further advanced the field by incorporating prior statistical information to refine parameter estimates. With over 170 cumulative citations, Presse's research remains essential for anyone working on robot simulation, control, or calibration—providing the theoretical and practical tools needed to bridge the gap between idealized models and physical hardware.
Research Focus
Key Achievements
Top Papers
- 1New criteria of exciting trajectories for robot identification123 citations · 2002
- 2Identification of inertial and drive gain parameters of robots22 citations · 2002
- 3Sequential identification of base parameters of robots11 citations · 1991
- 4Bayesian estimation of inertial parameters of robots8 citations · 2003
- 5Identification des parametres dynamiques des robots5 citations · 1994
- 6Experimental identification of dynamic parameters of robot2 citations · 1994