端到端的灵活臂-手VLA策略通过共享自主性：通过自主手VLA策略增强的VR远程操作用于高效数据收集
End-to-End Dexterous Arm-Hand VLA Policies via Shared Autonomy: VR Teleoperation Augmented by Autonomous Hand VLA Policy for Efficient Data Collection
AI总结：
本文提出了一种共享自主框架，通过结合VR远程操作和自主手VLA策略，实现高效的数据收集，从而训练端到端的VLA策略以提升机器人灵活操作能力。
摘要：
实现类似人类的灵活操作仍然是通用机器人的一大挑战。尽管视觉-语言-动作（VLA）模型在从演示中学习技能方面显示出潜力，但其可扩展性受到高质量训练数据稀缺的限制。现有数据收集方法面临固有限制：手动远程操作会过度负荷人类操作员，而自动规划往往产生不自然的动作。我们提出了一种共享自主框架，将控制分为宏观和微观动作。人类操作员通过直观的VR远程操作指导机器人手臂的姿态，而自主的DexGrasp-VLA策略利用实时触觉和视觉反馈处理精细的手部控制。这种分工显著减少了认知负担，并使高效收集高质量的协调手臂-手演示成为可能。使用这些数据，我们训练了一个端到端的VLA策略，该策略配备了我们新的手臂-手特征增强模块，该模块捕捉了宏观和微观动作的各自和共享表示，以实现更自然的协调。我们的纠正远程操作系统通过人机交互的故障恢复实现连续的策略改进。实验表明，我们的框架能够以最小的人力生成高质量的数据，并在各种物体上实现了90%的成功率，包括未见过的实例。全面的评估验证了系统在开发灵活操作能力方面的有效性。
英文摘要：
Achieving human-like dexterous manipulation remains a major challenge for general-purpose robots. While Vision-Language-Action (VLA) models show potential in learning skills from demonstrations, their scalability is limited by scarce high-quality training data. Existing data collection methods face inherent constraints: manual teleoperation overloads human operators, while automated planning often produces unnatural motions. We propose a Shared Autonomy framework that divides control between macro and micro motions. A human operator guides the robot's arm pose through intuitive VR teleoperation, while an autonomous DexGrasp-VLA policy handles fine-grained hand control using real-time tactile and visual feedback. This division significantly reduces cognitive load and enables efficient collection of high-quality coordinated arm-hand demonstrations. Using this data, we train an end-to-end VLA policy enhanced with our novel Arm-Hand Feature Enhancement module, which captures both distinct and shared representations of macro and micro movements for more natural coordination. Our Corrective Teleoperation system enables continuous policy improvement through human-in-the-loop failure recovery. Experiments demonstrate that our framework generates high-quality data with minimal manpower and achieves a 90% success rate across diverse objects, including unseen instances. Comprehensive evaluations validate the system's effectiveness in developing dexterous manipulation capabilities.
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。
