Papers
9
Total Citations
54
H-Index
4
About
Paolo Pagliuca’s research lies at the intersection of neuroevolution, swarm robotics, and human-robot interaction, with a focus on creating adaptive, robust autonomous systems. His most influential work, “Robust optimization through neuroevolution” (2019, 25 citations), introduced a method for evolving neural network controllers that can operate effectively in new environmental conditions without requiring adaptation—a key contribution to the field of evolutionary robotics. Pagliuca has also advanced swarm intelligence by evolving aggregation behaviors in robot groups using algorithms like OpenAI-ES, and has explored the synergy between learning and evolution, investigating how these processes can be combined to improve robotic performance. In the domain of healthcare robotics, his work on the MARIO robot demonstrated automatic gait analysis using the Timed Up and Go test, showcasing practical applications for elderly care. More recently, he has investigated the impact of educational robotics on early childhood social dynamics, broadening the scope of robotics research. With over 50 citations across his publications, Pagliuca’s contributions span theoretical foundations in neuroevolution to tangible applications in medicine and education, making him a versatile researcher in adaptive robotics and embodied intelligence.
Research Focus
Key Achievements
Top Papers
- 1Robust optimization through neuroevolution25 citations · 2019
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- 4Self-organized Aggregation in Group of Robots with OpenAI-ES6 citations · 2023
- 5Skeleton Timed Up and Go on MARIO Robot3 citations · 2023
- 6Learning and Evolution: Factors Influencing an Effective Combination2 citations · 2024
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- 8The dynamic of body and brain co-evolution2 citations · 2021
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