序列灵巧性：链式灵巧策略实现长时程操作
Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation
AI总结：
提出序列灵巧性系统，通过链式灵巧策略和过渡可行性函数，解决长时程操作任务，实现仿真到真实的零样本迁移。
摘要：
许多现实世界的操作任务由一系列彼此差异显著的子任务组成。这类长时程、复杂的任务凸显了灵巧手的潜力，其具备适应性和多功能性，能够无需重新抓取或外部工具即可在不同功能模式间无缝切换。然而，灵巧手的高维动作空间和长时程任务复杂的组合动态带来了挑战。我们提出了序列灵巧性（Sequential Dexterity），一个基于强化学习（RL）的通用系统，通过链式连接多个灵巧策略来实现长时程任务目标。该系统的核心是一个过渡可行性函数，该函数逐步微调子策略以提高链式成功率，同时还能实现自主策略切换，用于从失败中恢复以及绕过冗余阶段。尽管仅在仿真中使用少量任务物体进行训练，我们的系统展示了对新物体形状的泛化能力，并能够零样本迁移到配备灵巧手的真实机器人上。代码和视频可在 https://sequential-dexterity.github.io 获取。
英文摘要：
Many real-world manipulation tasks consist of a series of subtasks that are significantly different from one another. Such long-horizon, complex tasks highlight the potential of dexterous hands, which possess adaptability and versatility, capable of seamlessly transitioning between different modes of functionality without the need for re-grasping or external tools. However, the challenges arise due to the high-dimensional action space of dexterous hand and complex compositional dynamics of the long-horizon tasks. We present Sequential Dexterity, a general system based on reinforcement learning (RL) that chains multiple dexterous policies for achieving long-horizon task goals. The core of the system is a transition feasibility function that progressively finetunes the sub-policies for enhancing chaining success rate, while also enables autonomous policy-switching for recovery from failures and bypassing redundant stages. Despite being trained only in simulation with a few task objects, our system demonstrates generalization capability to novel object shapes and is able to zero-shot transfer to a real-world robot equipped with a dexterous hand. Code and videos are available at https://sequential-dexterity.github.io
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。
