We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place. Our benchmark uses the YCB objects, a commonly used dataset in the robotics community, to ensure that our results are comparable to other studies. Additionally, the benchmark is designed to be easily reproducible in the real world, making it accessible to researchers and practitioners. We also provide our experimental results and analyzes for model-based and model-free 6D robotic grasping on the benchmark, where representative algorithms are evaluated for object perception, grasping planning, and motion planning. We believe that our benchmark will be a valuable tool for advancing the field of robot manipulation. By providing a standardized evaluation framework, researchers can more easily compare different techniques and algorithms, leading to faster progress in developing robot manipulation methods.

翻译：我们提出了一种新的可重复基准测试方法，用于评估真实世界中的机器人操作，特别聚焦于抓取与放置任务。该基准测试采用机器人学界常用的YCB物体数据集，以确保结果具有跨研究可比性。此外，该基准测试的设计注重真实世界中的易复现性，使其适用于研究人员和实践者。我们同时提供了基于模型与无模型的六自由度机器人抓取实验分析与结果，针对物体感知、抓取规划及运动规划等环节对代表性算法进行了评估。我们认为该基准测试将成为推动机器人操作领域发展的重要工具：通过提供标准化的评估框架，研究人员能够更便捷地比较不同技术与算法，从而加速机器人操作方法的研究进程。