Lino Costa
Papers
18
Total Citations
162
H-Index
7
About
Lino Costa is a researcher whose work sits at the intersection of bio-inspired computation, evolutionary optimization, and robotic locomotion. His research centers on applying biologically motivated algorithms—particularly Central Pattern Generators (CPGs) and genetic algorithms—to solve complex locomotion challenges in both quadruped and biped robots. Costa's most significant contributions involve combining CPG-based motor control with multi-objective evolutionary algorithms to optimize robot gaits. His work on quadruped and biped locomotion optimization, accumulating citations across multiple studies published between 2009 and 2015, has advanced the field's understanding of how nature-inspired mechanisms can generate adaptive, efficient movement in robotic systems. Notably, his 2009 research on head motion stabilization during quadruped locomotion addressed a critical practical challenge—enabling stable visual perception during movement—combining dynamical systems with genetic algorithms to achieve this goal. Costa has also explored automatic locomotion controller generation through genetic programming, terrain adaptation in sloped environments, and skill memory in biped systems. More recently, his interests have expanded into Robotic Process Automation, applying multi-objective mathematical modeling to sustainable RPA implementation. With a body of work spanning foundational robotics challenges to emerging automation domains, Costa's research reflects a consistent commitment to optimization-driven solutions for complex, real-world robotic problems.
Research Focus
Key Achievements
Top Papers
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- 7Adapting Biped Locomotion to Sloped Environments11 citations · 2015
- 8Using Cost-regularized Kernel Regression with a high number of samples6 citations · 2014
- 9Skill Memory in Biped Locomotion6 citations · 2015
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