BulletArm: An Open-Source Robotic Manipulation Benchmark and Learning Framework
BulletArm: An Open-Source Robotic Manipulation Benchmark and Learning Framework
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DOI：
10.48550/arxiv.2205.14292
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Dian Wang;Colin Kohler;Xu Zhu;Ming Jia;Robert W. Platt
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文献类型：
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作者：
Dian Wang;Colin Kohler;Xu Zhu;Ming Jia;Robert W. Platt
我们提出了BulletArm，一个新的机器人操作基准和学习环境。BulletArm的设计围绕两个关键原则：可重复性和可扩展性。我们的目标是通过在模拟中提供一组标准化的基准任务以及一系列基线算法，鼓励机器人学习方法之间进行更直接的比较。该框架由31个不同难度的操作任务组成，从简单的伸手和拾取任务到更现实的任务，如装箱和托盘堆垛。除了提供的任务外，BulletArm还提供了一套工具来帮助用户在框架中添加新任务。此外，我们引入了一组五个基准，并使用一系列最先进的基线算法进行评估。通过将这些算法作为我们框架的一部分，我们希望鼓励用户根据这些基线对任何新任务进行基准测试。
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.