ORCA: 开源灵巧性研究平台
ORCA: A Platform for Open-Source Dexterity Research
AI总结：
提出ORCA学习栈，统一灵巧手控制、仿真、遥操作和重定向，集成机器人学习框架，实现端到端灵巧操作研究。
摘要：
机器人操作研究越来越关注两指平行夹爪，因其有效性、经济性和易于遥操作。然而，夹爪受限于其外形因素，即使对于简单的重新定向任务，也常常需要双臂设置。拟人手是灵巧机器人学习的更自然平台——更接近人手，能够从人类视频中学习——但它们在学习研究中仍然难以使用：即使存在开放且可访问的手部硬件，用于控制、仿真、遥操作和重定向的软件也分散在零散的代码库中，并且与机器人学习生态系统基本脱节。在这项工作中，我们介绍了\orca~学习栈，这是一个将灵巧性作为第一类机器人学习领域的开源研究栈。我们的\orca~栈将低级控制、仿真、来自一系列消费平台的遥操作以及手部重定向统一在单个接口后面，并原生集成流行的机器人学习框架（如\lerobot），使灵巧手研究人员能够利用与非灵巧机器人学习相同的数据、训练和评估流程。我们展示了一个完整的端到端工作流程，通过使用消费级VR头显进行遥操作收集手内重新定向任务的专家演示，使用\lerobot训练自主策略，并在完全可重现和可观察的设置中评估学习到的策略。我们将整个栈开源，作为灵巧操作研究的共享、可重现基础。
英文摘要：
Robotics manipulation research increasingly focuses on two-finger parallel grippers for their effectiveness, affordability, and ease of teleoperation. Grippers are nonetheless limited by their form factor, often requiring bimanual setups even for simple reorientation tasks. Anthropomorphic hands are a more natural platform for dexterous robot learning -- closer to the human hand, and capable of learning from human video -- yet they remain hard to use in learning research: even where open and accessible hand hardware exists, the software for control, simulation, teleoperation, and retargeting is scattered in one-off code bases, and largely disconnected from the robot-learning ecosystem. In this work, we introduce the \orca~learning stack, an open-source research stack for dexterity as a first-class robot learning domain. Our \orca~stack unifies low-level control, simulation, teleoperation from a range of consumer platforms, and hand retargeting, behind a single interface, and integrates natively with popular robot-learning frameworks such as \lerobot, so dexterous hand researchers can leverage the same data, training, and evaluation pipelines used for non-dexterous robot learning. We demonstrate a complete end-to-end workflow, collecting expert demonstrations of an in-hand reorientation task by teleoperation with a consumer-grade VR headset, training an autonomous policy with \lerobot, and evaluating the learned policy in a fully reproducible and observable setup. We open-source the entire stack as a shared, reproducible foundation for dexterous-manipulation research.
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