Alvaro Paz
Papers
2
Total Citations
12
H-Index
2
About
Alvaro Paz is a researcher specializing in robot trajectory optimization, numerical optimal control, and geometric algorithms, with a focus on leveraging Lie group methods for efficient motion planning. His major contributions lie in advancing the practical implementation of optimal control techniques for robotic systems, particularly through the careful handling of sparsity and transcription methods. In his 2019 work, "Practical guide to solve the minimum-effort problem with geometric algorithms and B-Splines," Paz addressed critical but often overlooked implementation issues in numerical optimal control, providing clear algorithms for discrete problem representation. His 2020 paper, "Exploiting sparsity in robot trajectory optimization with direct collocation and geometric algorithms," further advanced the field by demonstrating how inherent sparsity in direct collocation can be exploited to dramatically reduce computational costs, specifically minimizing floating-point operations for first-order information. While his citation counts (7 and 5, respectively) reflect a growing, specialized audience, these works are foundational for researchers seeking computationally efficient, real-world solutions to complex robotic motion problems. Paz’s work is particularly notable for bridging the gap between theoretical optimal control and practical algorithm design.
Research Focus
Key Achievements
Top Papers
- 1
- 2