# Bio-Inspired Event-Based Visual Servoing for Ground Robots

arXiv: https://arxiv.org/abs/2603.23672 · HTML: https://mlsanity.com/paper/2603.23672/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Neuromorphic Eye-in-Hand Visual Servoing](https://mlsanity.com/paper/2004.07398/) (2020)
- [SEBVS: Synthetic Event-based Visual Servoing for Robot Navigation and Manipulation](https://mlsanity.com/paper/2508.17643/) (2025)
- [An exact active sensing strategy for a class of bio-inspired systems](https://mlsanity.com/paper/2411.06612/) (2024)

## Abstract (arXiv)

Biological sensory systems are inherently adaptive, filtering out constant stimuli and prioritizing relative changes, likely enhancing computational and metabolic efficiency. Inspired by active sensing behaviors across a wide range of animals, this paper introduces a principled 1D event-based visual servoing framework for ground robots operating in structured environments. Utilizing a Dynamic Vision Sensor (DVS), we demonstrate that by applying a fixed spatial kernel to the asynchronous event stream generated from structured logarithmic intensity-change patterns, the resulting net event flux analytically isolates specific combinations of kinematic states. We establish a generalized theoretical bound for this event rate estimator and show that linear and quadratic spatial profiles isolate the robot's velocity and position-velocity product, respectively. Leveraging these properties, we employ a multi-pattern stimulus to directly synthesize a nonlinear state feedback term entirely without traditional state estimation. To overcome the inescapable loss of linear observability at equilibrium inherent in event sensing, we propose a bio-inspired active sensing limit-cycle controller. Experimental validation on a 1/10-scale autonomous ground vehicle confirms the efficacy, extreme low-latency, and computational efficiency of the proposed direct-sensing approach.