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About

M. Pollino is a pioneering researcher in bio-inspired robotics, specializing in the intersection of neuromorphic engineering and autonomous locomotion. Their work centers on developing neural control systems for robots, drawing direct inspiration from insect neurobiology—particularly the neural mechanisms underlying movement in fruit flies (Drosophila melanogaster) and the unsupervised learning rules of spiking neural networks. Pollino’s major contributions include the implementation of Spike Timing Dependent Plasticity (STDP) for behavior learning on the TriBot hybrid robot, a correlation-based navigation algorithm that allows robots to autonomously enhance tactic and phobic behaviors without supervision. They also advanced locomotion control by applying a CNN-based Central Pattern Generator (CPG) VLSI chip to a fully autonomous mini-hexapod robot, demonstrating stable, insect-like crawling with sensory feedback. Their most cited works (each with 22 citations) established foundational methods for embedding unsupervised learning directly into robotic hardware. Notably, Pollino’s research bridges theoretical neuroscience and practical robotics, creating systems that learn and adapt in real-time. Their work on fly-inspired sensory feedback for hexapod locomotion represents a significant step toward truly autonomous, biologically plausible robots. With a consistent citation record, Pollino’s contributions continue to influence the fields of neuromorphic computing, adaptive robotics, and bio-inspired control systems.