# DGBench: An Open-Source, Reproducible Benchmark for Dynamic Grasping

arXiv: https://arxiv.org/abs/2204.13879 · HTML: https://mlsanity.com/paper/2204.13879/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2022.

## Built on by (most cited first, 5 of 5)

- [FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation](https://mlsanity.com/paper/2305.12821/) (2023)
- [Exploring Embodied Multimodal Large Models: Development, Datasets, and Future Directions](https://mlsanity.com/paper/2502.15336/) (2025)
- [Overcoming Dynamics-Blindness: Training-Free Pace-and-Path Correction for VLA Models](https://mlsanity.com/paper/2605.11459/) (2026)
- [SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes](https://mlsanity.com/paper/2508.20547/) (2025)
- [DynaWM: A Base-VLA-Guided World Foundation Model for Moving-Object Manipulation](https://mlsanity.com/paper/2607.02604/) (2026)

## Abstract (arXiv)

This paper introduces DGBench, a fully reproducible open-source testing system to enable benchmarking of dynamic grasping in environments with unpredictable relative motion between robot and object. We use the proposed benchmark to compare several visual perception arrangements. Traditional perception systems developed for static grasping are unable to provide feedback during the final phase of a grasp due to sensor minimum range, occlusion, and a limited field of view. A multi-camera eye-in-hand perception system is presented that has advantages over commonly used camera configurations. We quantitatively evaluate the performance on a real robot with an image-based visual servoing grasp controller and show a significantly improved success rate on a dynamic grasping task.