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.

为了评估机器人在现实世界中的操作能力，我们提出了一个新的可重复使用的基准测试，着重于抓取和放置。我们使用机器人学界常用的 YCB 对象数据集来确保结果可与其他研究进行比较。此外，该基准测试旨在易于在实际环境中进行重现，使其可供研究人员和实践者使用。我们在基准测试中提供了基于模型和无模型 6D 机器人抓握的实验结果和分析，评估了代表性算法的物体感知、抓握规划和运动规划。我们相信，这个基准测试将是推进机器人操作领域的宝贵工具。通过提供标准化的评估框架，研究人员可以更容易地比较不同的技术和算法，从而更快地发展机器人操作方法。