Pierfrancesco Sueri

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About

Pierfrancesco Sueri is a robotics researcher whose work sits at the intersection of bio-inspired locomotion and advanced control theory. His primary research focuses on developing sophisticated control strategies for quadruped robots, particularly those employing Central Pattern Generators (CPGs)—neural architectures inspired by biological locomotion systems found in animals. Sueri’s major contributions include pioneering the integration of Model Predictive Control (MPC) with CPG-based locomotion, as demonstrated in his 2021 paper on MPC-based control of a neuro-inspired quadruped robot (10 citations). This work enables adaptive, real-time gait modulation by allowing the neural structure to respond to both proprioceptive and exteroceptive feedback. He further advanced the field by proposing a Neural Network Model Predictive Control (NNMPC) strategy—a data-driven, nonlinear approach that replaces traditional analytical models with learned dynamics, as detailed in his 2021 paper on a data-driven steering controller (5 citations). Though early in his career, Sueri’s innovative fusion of neural locomotion models with predictive control has established him as a promising voice in the quest for more agile, animal-like robotic movement.