Tendon-driven underactuated hands excel in adaptive grasping but often suffer from kinematic unpredictability and highly non-linear force transmission. This ambiguity limits their ability to perform precise free-motion shaping and deliver reliable payloads for complex manipulation tasks. To address this, we introduce the PHANTOM Hand (Hybrid Precision-Augmented Compliance): a modular, 1:1 human-scale system featuring 6 actuators and 15 degrees of freedom (DoFs). We propose a unified framework that bridges the gap between precise analytic shaping and robust compliant grasping. By deriving a sparse mapping from physical geometry and integrating a mechanics-based compensation model, we effectively suppress kinematic drift caused by spring counter-tension and tendon elasticity. This approach achieves sub-degree kinematic reproducibility for free-motion planning while retaining the inherent mechanical compliance required for stable physical interaction. Experimental validation confirms the system's capabilities through (1) kinematic analysis verifying sub-degree global accuracy across the workspace; (2) static expressibility tests demonstrating complex hand gestures; (3) diverse grasping experiments covering power, precision, and tool-use categories; and (4) quantitative fingertip force characterization. The results demonstrate that the PHANTOM hand successfully combines analytic kinematic precision with continuous, predictable force output, significantly expanding the payload and dexterity of underactuated hands. To drive the development of the underactuated manipulation ecosystem, all hardware designs and control scripts are fully open-sourced for community engagement.

翻译：肌腱驱动欠驱动手爪在自适应抓取中表现优异，但常受制于运动学不可预测性与高度非线性的力传递。这种不确定性限制了其在复杂操作任务中实现精确自由空间构型与可靠载荷输出的能力。为此，我们提出PHANTOM Hand（混合精度增强型柔性系统）：一种模块化、1:1人体尺寸比例的系统，配备6个驱动器和15个自由度（DoFs）。我们提出一个统一框架，弥合了精确解析构型与鲁棒顺应性抓取之间的差距。通过从物理几何结构中推导稀疏映射，并集成基于力学的补偿模型，有效抑制了弹簧反拉力与肌腱弹性引起的运动学漂移。该方法在自由运动规划中实现亚度级运动学可重复性，同时保留稳定物理交互所需的内在机械顺应性。实验验证通过以下四方面证实系统能力：(1)运动学分析验证工作空间内亚度级全局精度；(2)静态可表达性测试展示复杂手势；(3)涵盖力抓取、精确抓取与工具使用类别的多样化抓取实验；(4)定量指尖力表征。结果表明，PHANTOM Hand成功融合了解析运动学精度与连续可预测力输出，显著提升了欠驱动手爪的载荷能力与灵巧性。为推动欠驱动操作生态系统发展，所有硬件设计与控制脚本已完全开源供社区使用。