A Benchmarking Study of Vision-Based Robotic Grasping Algorithms: A Comparative Analysis
Abstract
We present a benchmarking study of vision-based robotic grasping algorithms and provide a comparative analysis. In particular, we compare two machine-learning-based and two analytical algorithms using an existing benchmarking protocol from the literature and determine the algorithms strengths and weaknesses under different experimental conditions. These conditions include variations in lighting, background textures, cameras with different noise levels, and grippers. We also run analogous experiments in simulations and with real robots and present the discrepancies. Some experiments are also run in two different laboratories using the same protocols to further analyze the repeatability of our results. We believe that this study, comprising 5040 experiments, provides important insights into the role and challenges of systematic experimentation in robotic manipulation and guides the development of new algorithms by considering the factors that could impact the performance. The experiment recordings and our benchmarking software are publicly available.
Cite
@article{arxiv.2307.11622,
title = {A Benchmarking Study of Vision-Based Robotic Grasping Algorithms: A Comparative Analysis},
author = {Bharath K Rameshbabu and Sumukh S Balakrishna and Brian Flynn and Vinayak Kapoor and Adam Norton and Holly Yanco and Berk Calli},
journal= {arXiv preprint arXiv:2307.11622},
year = {2025}
}
Comments
Accepted in IEEE Robotics and Automation Magazine 2025. Previously this version with slight modifications appeared as arXiv:2503.11163, which was submitted as a new work by accident. We have requested for removal of arXiv:2503.11163 from the other account, while simultaneously submitting this version as a revision to arXiv:2307.11622. We are stressing that this submission is intentional