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About

Santiago Paternain is a leading researcher in optimization and robotics, whose work bridges the gap between time-varying convex optimization and autonomous robotic systems. His major contributions include the development of time-structured algorithms for dynamic environments, notably the prediction-correction interior-point method for tracking optimal solutions in real-time—a foundational approach cited in his 2017 and 2020 papers. Paternain has also advanced industrial robotics through fast relative motion tracking for dual-arm setups, addressing critical needs in manufacturing processes like spraying and welding. His work on source seeking in unknown environments with obstacles and sufficiently accurate model learning for robot-environment interaction demonstrates a commitment to practical, real-world autonomy. With over 25 total citations across his most-cited papers, including recent 2024 and 2025 publications on joint trajectory optimization for redundant manipulators, Paternain’s research is gaining traction for its impact on both theoretical optimization and applied robotics. His bi-level optimization method for dual-arm minimum time problems represents a notable achievement, offering novel solutions to complex coordination challenges.