Robotic assembly remains a significant challenge due to complexities in visual perception, functional grasping, contact-rich manipulation, and performing high-precision tasks. Simulation-based learning and sim-to-real transfer have led to recent success in solving assembly tasks in the presence of object pose variation, perception noise, and control error; however, the development of a generalist (i.e., multi-task) agent for a broad range of assembly tasks has been limited by the need to manually curate assembly assets, which greatly constrains the number and diversity of assembly problems that can be used for policy learning. Inspired by recent success of using generative AI to scale up robot learning, we propose MatchMaker, a pipeline to automatically generate diverse, simulation-compatible assembly asset pairs to facilitate learning assembly skills. Specifically, MatchMaker can 1) take a simulation-incompatible, interpenetrating asset pair as input, and automatically convert it into a simulation-compatible, interpenetration-free pair, 2) take an arbitrary single asset as input, and generate a geometrically-mating asset to create an asset pair, 3) automatically erode contact surfaces from (1) or (2) according to a user-specified clearance parameter to generate realistic parts. We demonstrate that data generated by MatchMaker outperforms previous work in terms of diversity and effectiveness for downstream assembly skill learning. For videos and additional details, please see our project website: https://wangyian-me.github.io/MatchMaker/.

翻译：机器人装配由于视觉感知、功能性抓取、接触密集型操作以及执行高精度任务的复杂性，仍然是一项重大挑战。基于模拟的学习和模拟到现实的迁移，最近在解决存在物体姿态变化、感知噪声和控制误差的装配任务方面取得了成功；然而，开发适用于广泛装配任务的通用（即多任务）智能体，一直受到需要手动策划装配资产的限制，这极大地制约了可用于策略学习的装配问题的数量和多样性。受近期利用生成式人工智能扩展机器人学习规模的成功启发，我们提出了MatchMaker，这是一个自动生成多样化、模拟兼容的装配资产对的流程，以促进装配技能的学习。具体而言，MatchMaker能够：1）以一个模拟不兼容、相互穿透的资产对作为输入，自动将其转换为模拟兼容、无穿透的资产对；2）以任意单个资产作为输入，生成一个几何上配合的资产以创建资产对；3）根据用户指定的间隙参数，自动从（1）或（2）中的接触表面进行侵蚀，以生成逼真的零件。我们证明，MatchMaker生成的数据在多样性和下游装配技能学习的有效性方面均优于先前的工作。有关视频和更多详情，请访问我们的项目网站：https://wangyian-me.github.io/MatchMaker/。