Papers
5
Total Citations
53
H-Index
4
About
Sergio Aguilera’s research lies at the critical intersection of robotic safety, control theory, and autonomous task planning. His primary contributions focus on developing real-time control architectures that guarantee safety for robotic manipulators operating in dynamic, human-adjacent environments. By integrating Control Barrier Functions with Control Lyapunov Functions, Aguilera has pioneered methods that simultaneously satisfy task objectives and enforce safety constraints, even under torque saturation and input limitations—a challenge central to modern collaborative robotics. His most cited work (2022, 24 citations) presents a novel safety-compliant control framework that has become a reference for researchers addressing human-robot interaction. Aguilera has also extended his expertise into emerging areas, such as using Large Language Models for safety-aware task planning (2025, 8 citations), addressing the critical gap between LLM-driven reasoning and risk mitigation in long-horizon workflows. Earlier foundational work includes dynamic modeling of skid-steer mobile manipulators using spatial vector algebra, validated experimentally with a compact loader. With a growing citation footprint and contributions spanning from foundational dynamics to cutting-edge AI integration, Aguilera is shaping the future of safe, autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
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- 4Safety Aware Task Planning via Large Language Models in Robotics8 citations · 2025
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