Ioannis Polykretis

Rutgers, The State University of New Jersey, Accenture (United States)

Papers

Total Citations

H-Index

About

Ioannis Polykretis is a leading researcher at the intersection of neuromorphic computing, bioinspired robotics, and autonomous navigation. His work fundamentally reimagines how robotic systems can achieve fluid, human-like motion and intelligent navigation by drawing direct inspiration from biological neural networks. Polykretis’s major contributions include the development of a bioinspired smooth neuromorphic controller for robotic arms, which replicates the natural, jerk-free reaching movements of biological agents, and the creation of a spiking neural network that mimics the oculomotor system to control a biomimetic robotic head on neuromorphic hardware without requiring on-chip learning. His most cited paper (2023, 9 citations) advances smooth motion control, while his 2022 work (8 citations) demonstrates energy-efficient, low-latency robotic control. In 2024, he introduced a self-supervised cognitive map learner for mapless mobile robot navigation at the edge, achieving robust autonomy without pre-mapped environments. Collectively, his papers have garnered over 23 citations, reflecting a growing impact in neuromorphic robotics. Polykretis’s work is notable for bridging theoretical neuroscience with practical, hardware-constrained robotic systems, paving the way for more adaptive, efficient, and biologically plausible autonomous agents.