编辑丨机器之心 点击下方 卡片 ，关注" 具身智能之心 "公众号 更多干货，欢迎加入国内首个具身智能全栈学习社区： 具身智能之心知识星球（戳我） ，这里包含所有你想要的! 实现通用机器人的类人灵巧操作能力，是机器人学领域长期以来的核心挑战之一。近年来，视觉 - 语言 - 动作 (Vision-Language-Action，VLA) 模型在机器人技能学 习方面展现出显著潜力，但其发展受制于一个根本性瓶颈： 高质量操作数据的获取。 ByteDance Seed 团队最新的研究论文《End-to-End Dexterous Arm-Hand VLA Policies via Shared Autonomy》[1]，针对这一关键问题提出了解决方案。 该研究的核心贡献在于提出了共享自主 (Shared Autonomy) 框架，通过合理划分人类操作员与自主 AI 系统的控制职责——人通过 VR 遥操作控制机械臂 (负责高层 定位和避障)，DexGrasp-VLA 自主控制灵巧手 (负责精细抓握)，消除了同时遥操作臂和灵巧手的需求，大幅降低操作员认知负荷，有效解决了机器人部署中最关 键的数据采集成本问题。通过将数据采集 ...

Core Insights - The article discusses the significant advancements in achieving dexterous manipulation capabilities in robotics through the Vision-Language-Action (VLA) model, addressing the critical challenge of high-quality data acquisition for training these models [2][6]. Group 1: Key Contributions - The research introduces a Shared Autonomy framework that effectively divides control responsibilities between human operators and autonomous AI systems, significantly reducing cognitive load and data collection costs [2][12][15]. - The DexGrasp-VLA strategy is highlighted as a foundational element of the Shared Autonomy framework, integrating multimodal inputs including tactile feedback, which enhances the robot's ability to adaptively grasp objects [9][20]. - The study establishes a complete technical system composed of four core modules, achieving a closed-loop from data collection to policy optimization [5][8]. Group 2: Data Collection and Efficiency - The Shared Autonomy framework has improved the efficiency of high-quality data collection by 25%, allowing for more data to be collected per hour and compressing the development-deployment cycle to under one day [33]. - The framework has demonstrated a near-industrial standard performance with approximately 90% success rate in grasping over 50 different objects, facilitating the transition of dexterous manipulation technology from concept validation to practical deployment [33]. Group 3: Mechanisms and Enhancements - The Arm-Hand Feature Enhancement module is designed to model and integrate the kinematic differences between the arm and hand, resulting in more natural and robust coordination of macro and micro actions [16][19]. - The Corrective Human-in-the-Loop mechanism allows the robot to learn from failures by incorporating human demonstrations of correct actions, continuously improving the strategy and generalizing to edge cases [20][34]. Group 4: Future Directions - Future research directions include expanding the framework to more complex tasks such as object reorientation and precise placement, as well as exploring intelligent fusion mechanisms to address challenges in tactile feedback [36]. - The potential for autonomous error recognition and recovery through reinforcement learning is also discussed, aiming for a smooth transition from human-robot collaboration to full autonomy [36].