Papers
4
Total Citations
28
H-Index
3
About
David Galdeano is a robotics researcher whose work focuses on advancing the control and locomotion of humanoid robots, with a particular emphasis on achieving stable, human-like walking and whole-body coordination. His major contributions lie in the development of sophisticated pattern generators and control architectures that bridge the gap between theoretical dynamics and real-world application. Notably, his 2013 paper on an optimal ZMP-based pattern generator, which employs a Three-Mass Linear Inverted Pendulum Model (3MLIPM) for simplified yet effective bipedal dynamics, has garnered 10 citations and remains a foundational reference for dynamic walking. Galdeano also pioneered a task-based whole-body control strategy that integrates center-of-mass regulation with ZMP constraints and joint-limit avoidance, enabling complex motions like squatting. His 2021 work introduces a hybrid kinematic/dynamic control scheme that unifies operational and joint-space tracking for enhanced real-time performance. With over 28 cumulative citations across his most influential papers, Galdeano’s research continues to shape the practical implementation of stable, versatile humanoid robots.
Research Focus
Key Achievements
Top Papers
- 1
- 2Optimal pattern generator for dynamic walking in humanoid robotics10 citations · 2013
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