Recently, there has been a wealth of development in motion planning for robotic manipulation new motion planners are continuously proposed, each with their own unique strengths and weaknesses. However, evaluating new planners is challenging and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present MotionBenchMaker, an open-source tool to generate benchmarking datasets for realistic robot manipulation problems. MotionBenchMaker is designed to be an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms. Empirically, we show the benefit of using MotionBenchMaker as a tool to procedurally generate datasets which helps in the fair evaluation of planners. We also present a suite of 40 prefabricated datasets, with 5 different commonly used robots in 8 environments, to serve as a common ground to accelerate motion planning research.
翻译:最近,在机器人操纵的动态规划方面出现了大量动态发展,不断提出新的运动规划者,他们各自都有独特的优势和弱点。然而,评价新规划者具有挑战性,研究人员常常在基准制定方面产生自己的特别问题,这耗费时间,容易产生偏向,而且与其他最先进的规划者没有直接比较。我们提出了运动BenchMaker,这是一个开放的源码工具,用于为现实的机器人操纵问题生成基准数据集。动议BenchMaker设计了一种可扩展的、易于使用的工具,使用户既能生成数据集,又能通过比较运动规划算法来确定基准。我们很生动地展示了利用MotionBenchMaker这一工具在程序上产生数据集的好处,以帮助对规划者进行公平的评估。我们还提出了一套40个预设数据集,在8个环境中有5个常用的机器人,作为共同点,用以加速动作规划研究。