Objects with large base areas become ungraspable when they exceed the end-effector's maximum aperture. Existing approaches address this limitation through extrinsic dexterity, which exploits environmental features for non-prehensile manipulation. While grippers have shown some success in this domain, dexterous hands offer superior flexibility and manipulation capabilities that enable richer environmental interactions, though they present greater control challenges. Here we present ExDex, a dexterous arm-hand system that leverages reinforcement learning to enable non-prehensile manipulation for grasping ungraspable objects. Our system learns two strategic manipulation sequences: relocating objects from table centers to edges for direct grasping, or to walls where extrinsic dexterity enables grasping through environmental interaction. We validate our approach through extensive experiments with dozens of diverse household objects, demonstrating both superior performance and generalization capabilities with novel objects. Furthermore, we successfully transfer the learned policies from simulation to a real-world robot system without additional training, further demonstrating its applicability in real-world scenarios. Project website: https://tangty11.github.io/ExDex/.

翻译：当物体基底面积过大，超过末端执行器的最大开合范围时，便成为不可抓取物体。现有方法通过外部灵巧性来应对这一局限，即利用环境特征进行非抓取式操作。尽管夹爪在此领域已取得一定成功，但灵巧手凭借其卓越的灵活性与操作能力，能实现更丰富的环境交互，尽管其控制也面临更大挑战。本文提出ExDex，一个基于强化学习的灵巧臂-手系统，旨在实现对不可抓取物体的非抓取式操作以完成抓取。我们的系统学习两种策略性操作序列：将物体从桌面中心重新定位至边缘以便直接抓取，或移动至墙面，通过环境交互利用外部灵巧性实现抓取。我们通过对数十种不同家居物体的大量实验验证了该方法，证明了其在处理新物体时具有优越的性能和泛化能力。此外，我们成功地将习得的策略从仿真环境迁移到真实机器人系统，无需额外训练，进一步证明了其在现实场景中的适用性。项目网站：https://tangty11.github.io/ExDex/。