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

TL;DR

This paper conducts a comprehensive benchmarking of vision-based robotic grasping algorithms, comparing machine-learning and analytical methods across various conditions to identify their strengths, weaknesses, and reproducibility issues.

Contribution

It provides a large-scale, systematic comparison of grasping algorithms using a standardized protocol, including real and simulated experiments across multiple laboratories.

Findings

Machine-learning algorithms outperform analytical ones in certain lighting conditions.

Performance varies significantly with environmental factors like background and noise.

Reproducibility of results is affected by laboratory-specific variables.

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.