# Neuromorphic Eye-in-Hand Visual Servoing

arXiv: https://arxiv.org/abs/2004.07398 · HTML: https://mlsanity.com/paper/2004.07398/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers](https://mlsanity.com/paper/2112.09685/) (2021)
- [E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras](https://mlsanity.com/paper/2306.09078/) (2023)
- [A Neuromorphic Dataset for Object Segmentation in Indoor Cluttered Environment](https://mlsanity.com/paper/2302.06301/) (2023)
- [Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic Grasping](https://mlsanity.com/paper/2303.11228/) (2023)
- [E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes](https://mlsanity.com/paper/2604.04834/) (2026)
- [Event-VLA: Action-Conditioned Event Fusion for Robust Vision-Language-Action Model](https://mlsanity.com/paper/2606.29384/) (2026)
- [SE-Harris and eSUSAN: Asynchronous Event-Based Corner Detection Using Megapixel Resolution CeleX-V Camera](https://mlsanity.com/paper/2105.00480/) (2021)
- [Energy Aware Camera Location Search Algorithm for Increasing Precision of Observation in Automated Manufacturing](https://mlsanity.com/paper/2506.10251/) (2025)
- [Bio-Inspired Event-Based Visual Servoing for Ground Robots](https://mlsanity.com/paper/2603.23672/) (2026)
- [DSDyn-VLA: A Dual-Stream Dynamic Manipulation Framework with Motion Perception, Future Awareness, and Realtime Correction](https://mlsanity.com/paper/2609.39198/) (2026)

## Abstract (arXiv)

Robotic vision plays a major role in factory automation to service robot applications. However, the traditional use of frame-based camera sets a limitation on continuous visual feedback due to their low sampling rate and redundant data in real-time image processing, especially in the case of high-speed tasks. Event cameras give human-like vision capabilities such as observing the dynamic changes asynchronously at a high temporal resolution (1μ s) with low latency and wide dynamic range. In this paper, we present a visual servoing method using an event camera and a switching control strategy to explore, reach and grasp to achieve a manipulation task. We devise three surface layers of active events to directly process stream of events from relative motion. A purely event based approach is adopted to extract corner features, localize them robustly using heat maps and generate virtual features for tracking and alignment. Based on the visual feedback, the motion of the robot is controlled to make the temporal upcoming event features converge to the desired event in spatio-temporal space. The controller switches its strategy based on the sequence of operation to establish a stable grasp. The event based visual servoing (EVBS) method is validated experimentally using a commercial robot manipulator in an eye-in-hand configuration. Experiments prove the effectiveness of the EBVS method to track and grasp objects of different shapes without the need for re-tuning.