Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.23152v1</id>\n    <title>PHANTOM Hand</title>\n    <updated>2026-03-24T12:52:18Z</updated>\n    <link href='https://arxiv.org/abs/2603.23152v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.23152v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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).\n  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.\n  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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n    <published>2026-03-24T12:52:18Z</published>\n    <arxiv:comment>8 pages. Submitted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026</arxiv:comment>\n    <arxiv:primary_category term='cs.RO'/>\n    <author>\n      <name>Teng Yan</name>\n    </author>\n    <author>\n      <name>Jiongxu Chen</name>\n    </author>\n    <author>\n      <name>Qixiang Hua</name>\n    </author>\n    <author>\n      <name>Yue Yu</name>\n    </author>\n    <author>\n      <name>Zihang Wang</name>\n    </author>\n    <author>\n      <name>Yaohua Liu</name>\n    </author>\n    <author>\n      <name>Bingzhuo Zhong</name>\n    </author>\n  </entry>"
}