Today, selecting an optimal robot, its base pose, and trajectory for a given task is currently mainly done by human expertise or trial and error. To evaluate automatic approaches to this combined optimization problem, we introduce a benchmark suite encompassing a unified format for robots, environments, and task descriptions. Our benchmark suite is especially useful for modular robots, where the multitude of robots that can be assembled creates a host of additional parameters to optimize. We include tasks such as machine tending and welding in completely synthetic environments and 3D scans of real-world machine shops. The benchmark suite defines these optimization problems and facilitates the comparison of solution algorithms. All benchmarks are accessible through cobra.cps.cit.tum.de, a platform to conveniently share, reference, and compare tasks, robot models, and solutions.

翻译：当前，针对特定任务选择最优机器人及其基座位姿与运动轨迹，主要依赖人工经验或试错法。为评估针对这一组合优化问题的自动化方法，我们提出了一套包含统一格式的机器人模型、环境配置与任务描述的基准测试套件。该套件对模块化机器人尤为适用——这类机器人通过组装可生成庞大的候选参数空间。我们纳入了机器维护和焊接等任务场景，涵盖完全合成的虚拟环境与真实车间三维扫描数据。该基准测试套件定义了此类优化问题，并促进了解算法间的对比。所有基准测试均可通过cobra.cps.cit.tum.de平台访问，该平台便于任务描述、机器人模型及方案的共享、引用与比较。