对比四类抓取算法在多种条件下的表现，揭示系统性实验的挑战。
A Benchmarking Study of Vision-based Robotic Grasping Algorithms
- 对比学习型与解析型算法在光照、背景等条件下的性能差异。
- 5040次实验发现真实与仿真环境存在显著差距。
- 多实验室复现验证结果可靠性，适合算法开发者参考。
我们对基于视觉的机器人抓取算法进行了基准测试，涵盖两类机器学习方法和两类解析方法，并采用文献中的基准协议进行比较分析。实验考察了光照变化、背景纹理、不同噪声水平摄像头及夹爪等条件下的表现，同时在仿真与真实机器人上开展对比，揭示了二者间的差异。部分实验在两个不同实验室中重复执行，以评估结果的可复现性。本研究共完成5040次实验，为理解系统性实验在机器人操作中的作用与挑战提供了重要见解，并指导新算法开发时考虑影响性能的关键因素。实验数据与基准测试软件已公开。
原文摘要 · Abstract (English)
We present a benchmarking study of vision-based robotic grasping algorithms with distinct approaches, 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 algorithm's 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 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.
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