Ekaterina Gribkova

University of Illinois Urbana-Champaign, Intel (United States)

Papers

Total Citations

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About

Ekaterina Gribkova is a researcher at the intersection of soft robotics, biomechanics, and neuromorphic computing. Her most influential work, "Energy Shaping Control of a CyberOctopus Soft Arm" (2020, 36 citations), pioneers the application of energy shaping methodology to control flexible, elastic Cosserat rod models—a critical advance for understanding embodiment and mechanics in biological control systems. This foundational contribution addresses the growing need for principled control strategies in continuum soft robots, where traditional rigid-body approaches fall short. More recently, Gribkova has pushed into visual intelligence with "LoCS-Net: Localizing convolutional spiking neural network for fast visual place recognition" (2025), introducing a bio-inspired spiking neural network that tackles the persistent challenges of perceptual aliasing and viewpoint variation in visual place recognition. Her work bridges theoretical control theory, embodied intelligence, and efficient neural computation, demonstrating a rare ability to connect fundamental mechanics with cutting-edge machine learning. Gribkova’s research is shaping how robots perceive and move in unstructured environments, making her a rising voice in embodied AI and soft robotic control.