From Vision to Decision: Neuromorphic Control for Autonomous Navigation and Tracking
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911424409763840 |
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| author | Wang, Chuwei Sebastián, Eduardo Prorok, Amanda Bizyaeva, Anastasia |
| author_facet | Wang, Chuwei Sebastián, Eduardo Prorok, Amanda Bizyaeva, Anastasia |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_05683 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Vision to Decision: Neuromorphic Control for Autonomous Navigation and Tracking Wang, Chuwei Sebastián, Eduardo Prorok, Amanda Bizyaeva, Anastasia Robotics Systems and Control 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. |
| title | From Vision to Decision: Neuromorphic Control for Autonomous Navigation and Tracking |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2602.05683 |