论文标题
Bulletarm：开源机器人操纵基准和学习框架
BulletArm: An Open-Source Robotic Manipulation Benchmark and Learning Framework
论文作者
论文摘要
我们提出了Bulletarm，这是一种新颖的基准和用于机器人操纵的学习环境。 Bulletarm围绕两个关键原则设计：可重复性和可扩展性。我们旨在通过在模拟中提供一组标准化的基准任务以及基线算法的集合来鼓励机器人学习方法之间进行更直接的比较。该框架包括31个不同的操纵任务，这些任务有不同的困难，从简单到达和选择任务到更现实的任务，例如垃圾箱和托盘堆叠。除了提供的任务外，Bulletarm还建立了易于扩展，并提供了一套工具来帮助用户在框架中添加新任务时。此外，我们介绍了一组五个基准，并使用一系列最先进的基线算法对其进行了评估。通过将这些算法作为我们框架的一部分，我们希望鼓励用户在针对这些基线的任何新任务上进行基准测试。
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.
