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arXiv · 2602.05683

From Vision to Decision: Neuromorphic Control for Autonomous Navigation and Tracking

Chuwei Wang·Eduardo Sebastián·Amanda Prorok·Anastasia Bizyaeva
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Abstract

Robotic navigation has historically struggled to reconcile reactive, sensor-based control with the decisive capabilities of model-based planners. This duality becomes critical when the absence of a predominant option among goals leads to indecision, challenging reactive systems to break symmetries without computationally-intense planners. We propose a parsimonious neuromorphic control framework that bridges this gap for vision-guided navigation and tracking. Image pixels from an onboard camera are encoded as inputs to dynamic neuronal populations that directly transform visual target excitation into egocentric motion commands. A dynamic bifurcation mechanism resolves indecision by delaying commitment until a critical point induced by the environmental geometry. Inspired by recently proposed mechanistic models of animal cognition and opinion dynamics, the neuromorphic controller provides real-time autonomy with a minimal computational burden, a small number of interpretable parameters, and can be seamlessly integrated with application-specific image processing pipelines. We validate our approach in simulation environments as well as on an experimental quadrotor platform.

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Chuwei Wang, Eduardo Sebastián, Amanda Prorok, Anastasia Bizyaeva. 2026-02-05. From Vision to Decision: Neuromorphic Control for Autonomous Navigation and Tracking. https://arxiv.org/abs/2602.05683

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