灵巧的非抓取操作：通过外部灵巧性处理不可抓取物体
Dexterous Non-Prehensile Manipulation for Ungraspable Object via Extrinsic Dexterity
AI总结：
针对基底面积过大的不可抓取物体，提出基于强化学习的灵巧臂手系统ExDex，通过将物体移至边缘或墙壁利用外部灵巧性实现非抓取操作，实验验证性能优越且可泛化至新物体，并成功迁移至真实机器人。
摘要：
当物体的基底面积超过末端执行器的最大开口时，这些物体变得不可抓取。现有方法通过外部灵巧性来解决这一限制，即利用环境特征进行非抓取操作。虽然夹爪在该领域已取得一定成功，但灵巧手提供了更优越的灵活性和操作能力，能够实现更丰富的环境交互，尽管它们带来了更大的控制挑战。在此，我们提出了ExDex，一个灵巧的臂手系统，利用强化学习实现不可抓取物体的非抓取操作。我们的系统学习两种策略性操作序列：将物体从桌子中心移至边缘以直接抓取，或移至墙壁，通过环境交互利用外部灵巧性实现抓取。我们通过数十种多样化家用物体的广泛实验验证了我们的方法，展示了优越的性能和对新物体的泛化能力。此外，我们成功地将学习到的策略从仿真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/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
