Learning generalizable manipulation skills is central for robots to achieve task automation in environments with endless scene and object variations. However, existing robot learning environments are limited in both scale and diversity of 3D assets (especially of articulated objects), making it difficult to train and evaluate the generalization ability of agents over novel objects. In this work, we focus on object-level generalization and propose SAPIEN Manipulation Skill Benchmark (abbreviated as ManiSkill), a large-scale learning-from-demonstrations benchmark for articulated object manipulation with visual input (point cloud and image). ManiSkill supports object-level variations by utilizing a rich and diverse set of articulated objects, and each task is carefully designed for learning manipulations on a single category of objects. We equip ManiSkill with high-quality demonstrations to facilitate learning-from-demonstrations approaches and perform evaluations on common baseline algorithms. We believe ManiSkill can encourage the robot learning community to explore more on learning generalizable object manipulation skills.

中文翻译：

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ManiSkill：从演示中学习可推广操作技能的基准

学习通用的操作技能是机器人在场景和物体变化无穷的环境中实现任务自动化的核心。然而，现有的机器人学习环境在 3D 资产（尤其是铰接对象）的规模和多样性方面受到限制，这使得很难训练和评估代理对新对象的泛化能力。在这项工作中，我们专注于对象级泛化并提出 SAPIEN 操作技能基准（缩写为 ManiSkill），这是一个大规模的从演示中学习的基准，用于通过视觉输入（点云和图像）进行铰接对象操作。ManiSkill 通过利用丰富多样的铰接对象集支持对象级别的变化，每个任务都经过精心设计，用于学习对单一类别对象的操作。我们为 ManiSkill 配备了高质量的演示，以促进从演示中学习的方法，并对常见的基线算法进行评估。我们相信 ManiSkill 可以鼓励机器人学习社区探索更多关于学习通用对象操作技能的知识。