Bio-Inspired Event-Based Visual Servoing for Ground Robots

• URL: http://arxiv.org/abs/2603.23672v1
• Date: Tue, 24 Mar 2026 19:20:20 GMT
• Title: Bio-Inspired Event-Based Visual Servoing for Ground Robots
• Abstract summary: We present a novel event-based visual servoing framework for ground robots.<n>Inspired by active sensing behaviors across a wide range of animals, we propose a bio-inspired active sensing limit-cycle controller.
• Score: 4.2300250650442095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 presents a novel event-based visual servoing framework for ground robots. 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 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.

Related papers

• An event-driven framework for fly-inspired visual motion detection [9.756242900662377]
  This work integrates emerging event-based sensing with biologically structured neural computation to establish an efficient computational paradigm for visual motion detection.<n>The proposed framework is built upon a recently developed fly-inspired neural network that emulates motion-processing circuits in the optic lobe.<n> Experimental results demonstrate that the framework effectively combines the temporal advantages of event-driven vision with the efficiency and interpretability of bio-inspired neural processing.
  arXiv  Detail & Related papers  (2026-07-06T15:22:44Z)
• Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing [73.88859384645264]
  Out-Of-Distribution (OOD) generalization to various racetracks utilizes Machine Learning (ML) to encode the mathematical relation between sensor data and vehicle actuation for end-to-end control.<n>This paper presents a DRL method that parameterizes nonlinear vehicle dynamics from the spectral distribution of depth measurements with a non-geometric, physics-informed reward, to infer vehicle time-optimal and overtaking racing controls with an Artificial Neural Network (ANN)<n>The policy outperforms human demonstrations by 12% in OOD tracks on proportionally scaled hardware, by maximizing the friction circle with tire dynamics that resemble an empirical Pacejka tire model.
  arXiv  Detail & Related papers  (2026-04-10T17:12:07Z)
• Hebbian Attractor Networks for Robot Locomotion [44.84033948079516]
  Hebbian plasticity offers a promising path toward rapid adaptation in changing environments.<n>Hebbian Attractor Networks (HANs) induce emergent attractor dynamics.<n>Results show that slower updates, combined with averaged pre- and postsynaptic activations, promote convergence to stable weight configurations.
  arXiv  Detail & Related papers  (2026-03-23T19:20:14Z)
• Collaborative Temporal Feature Generation via Critic-Free Reinforcement Learning for Cross-User Sensor-Based Activity Recognition [16.776182784171713]
  Human Activity Recognition using wearable inertial sensors is foundational to healthcare monitoring, fitness analytics, and context-aware computing.<n>Existing domain generalization approaches either neglect temporal dependencies in sensor streams or depend on impractical target-domain annotations.<n>We propose a new paradigm: modeling generalizable feature extraction as a collaborative sequential generation process governed by reinforcement learning.
  arXiv  Detail & Related papers  (2026-03-17T01:03:21Z)
• Enhancing Eye Feature Estimation from Event Data Streams through Adaptive Inference State Space Modeling [68.1289208938377]
  Eye feature extraction from event-based data streams can be performed efficiently and with low energy consumption.<n>We introduce the emphadaptive inference state space model (AISSM), a novel architecture for feature extraction.<n>We also craft and evaluate a novel learning technique that improves training efficiency.
  arXiv  Detail & Related papers  (2026-03-14T18:47:08Z)
• The power of dynamic causality in observer-based design for soft sensor applications [0.7965327033045845]
  This paper introduces a novel framework for optimizing observer-based soft sensors through dynamic causality analysis.<n>Traditional approaches to sensor selection often rely on linearized observability indices or statistical correlations that fail to capture the temporal evolution of complex systems.
  arXiv  Detail & Related papers  (2025-09-14T16:27:58Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• Deep Recurrence for Dynamical Segmentation Models [0.0]
  We propose a predictive coding inspired feedback mechanism that introduces a recurrent loop from output to input, allowing the model to refine its internal state over time.<n>We implement this mechanism within a standard U-Net architecture and introduce two biologically motivated operations, softmax projection and exponential decay, to ensure stability of the feedback loop.<n>We show that the feedback model significantly outperforms its feedforward counterpart in noisy conditions and generalizes more effectively with limited supervision.
  arXiv  Detail & Related papers  (2025-07-14T10:41:07Z)
• Allostatic Control of Persistent States in Spiking Neural Networks for perception and computation [79.16635054977068]
  We introduce a novel model for updating perceptual beliefs about the environment by extending the concept of Allostasis to the control of internal representations.<n>In this paper, we focus on an application in numerical cognition, where a bump of activity in an attractor network is used as a spatial numerical representation.
  arXiv  Detail & Related papers  (2025-03-20T12:28:08Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment [60.31418655784291]
  We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
  arXiv  Detail & Related papers  (2021-03-06T09:10:03Z)
• Wave Propagation of Visual Stimuli in Focus of Attention [77.4747032928547]
  Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to most relevant locations in the visual field. We present a biologically-plausible model of focus of attention that exhibits effectiveness and efficiency exhibited by foveated animals.
  arXiv  Detail & Related papers  (2020-06-19T09:33:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.