A Benchmarking Study of Vision-Based Robotic Grasping Algorithms: A Comparative Analysis

We present a benchmarking study of vision-based robotic grasping algorithms and provide a comparative analysis. In particular, we compare two machine learning (ML)-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 were also run in two different laboratories using the same protocols to further analyze the repeatability of our results. We believe that this study, comprising 5,040 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.