We present BulletArm, a novel benchmark and learning-environment for robotic manipulation. BulletArm is designed around two key principles: reproducibility and extensibility. We aim to encourage more direct comparisons between robotic learning methods by providing a set of standardized benchmark tasks in simulation alongside a collection of baseline algorithms. The framework consists of 31 different manipulation tasks of varying difficulty, ranging from simple reaching and picking tasks to more realistic tasks such as bin packing and pallet stacking. In addition to the provided tasks, BulletArm has been built to facilitate easy expansion and provides a suite of tools to assist users when adding new tasks to the framework. Moreover, we introduce a set of five benchmarks and evaluate them using a series of state-of-the-art baseline algorithms. By including these algorithms as part of our framework, we hope to encourage users to benchmark their work on any new tasks against these baselines.
翻译:我们介绍“BulletArm”,这是用于机器人操纵的新型基准和学习环境。“BulletArm”是围绕两个关键原则设计的:可复制性和可推广性。我们的目标是鼓励更直接地比较机器人学习方法,在模拟时提供一套标准化的基准任务,同时收集基线算法。框架包括31项不同的操作任务,难度不一,从简单到达和挑选任务到更现实的任务,如垃圾包装和托盘堆放等。除了提供的任务外,“BullArm”也是为了便于扩展,并提供了一套工具,帮助用户在为框架添加新任务时提供协助。此外,我们引入了一套五种基准,并利用一系列最先进的基线算法来评估这些基准。通过将这些算法作为我们框架的一部分,我们希望鼓励用户参照这些基线来衡量他们关于任何新任务的工作。