# Real-Time Grasping Strategies Using Event Camera

arXiv: https://arxiv.org/abs/2107.07200 · HTML: https://mlsanity.com/paper/2107.07200/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2021.

## Built on by (most cited first, 8 of 8)

- [Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers](https://mlsanity.com/paper/2112.09685/) (2021)
- [A Neuromorphic Dataset for Object Segmentation in Indoor Cluttered Environment](https://mlsanity.com/paper/2302.06301/) (2023)
- [Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control](https://mlsanity.com/paper/2302.13796/) (2023)
- [SEBVS: Synthetic Event-based Visual Servoing for Robot Navigation and Manipulation](https://mlsanity.com/paper/2508.17643/) (2025)
- [E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes](https://mlsanity.com/paper/2604.04834/) (2026)
- [A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation](https://mlsanity.com/paper/2404.17335/) (2024)
- [ECHO: Event-Augmented Context with Hindsight and Outlook for Wrist-Only Manipulation](https://mlsanity.com/paper/2609.34893/) (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 key role for perceiving the environment in grasping applications. However, the conventional framed-based robotic vision, suffering from motion blur and low sampling rate, may not meet the automation needs of evolving industrial requirements. This paper, for the first time, proposes an event-based robotic grasping framework for multiple known and unknown objects in a cluttered scene. Compared with standard frame-based vision, neuromorphic vision has advantages of microsecond-level sampling rate and no motion blur. Building on that, the model-based and model-free approaches are developed for known and unknown objects' grasping respectively. For the model-based approach, event-based multi-view approach is used to localize the objects in the scene, and then point cloud processing allows for the clustering and registering of objects. Differently, the proposed model-free approach utilizes the developed event-based object segmentation, visual servoing and grasp planning to localize, align to, and grasp the targeting object. The proposed approaches are experimentally validated with objects of different sizes, using a UR10 robot with an eye-in-hand neuromorphic camera and a Barrett hand gripper. Moreover, the robustness of the two proposed event-based grasping approaches are validated in a low-light environment. This low-light operating ability shows a great advantage over the grasping using the standard frame-based vision. Furthermore, the developed model-free approach demonstrates the advantage of dealing with unknown object without prior knowledge compared to the proposed model-based approach.