Achieving human-like dexterous manipulation remains a crucial area of research in robotics. Current research focuses on improving the success rate of pick-and-place tasks. Compared with pick-and-place, throw-catching behavior has the potential to increase picking speed without transporting objects to their destination. However, dynamic dexterous manipulation poses a major challenge for stable control due to a large number of dynamic contacts. In this paper, we propose a Stability-Constrained Reinforcement Learning (SCRL) algorithm to learn to catch diverse objects with dexterous hands. The SCRL algorithm outperforms baselines by a large margin, and the learned policies show strong zero-shot transfer performance on unseen objects. Remarkably, even though the object in a hand facing sideward is extremely unstable due to the lack of support from the palm, our method can still achieve a high level of success in the most challenging task. Video demonstrations of learned behaviors and the code can be found on the supplementary website.

翻译：实现类人灵巧操作仍是机器人领域的关键研究方向。当前研究主要聚焦于提升抓取-放置任务的成功率。与抓取-放置相比，抛接行为具有无需运输物体至目的地即可提升抓取速度的潜力。然而，由于大量动态接触的存在，动态灵巧操作对稳定控制构成了重大挑战。本文提出一种稳定性约束强化学习（SCRL）算法，用于学习使用灵巧手抓取多样物体。SCRL算法在性能上大幅超越基线方法，且习得策略在未见物体上展现出强大的零样本迁移能力。值得注意的是，即使物体处于手掌侧向支撑缺失的极端不稳定状态下，我们的方法仍能在最具挑战性的任务中取得高成功率。习得行为的视频演示与代码详见补充网站。