PDS Joint: A Parametric Double-Spiral Joint Tailored for Dexterous Hands

• URL: http://arxiv.org/abs/2606.24377v1
• Date: Tue, 23 Jun 2026 10:09:02 GMT
• Title: PDS Joint: A Parametric Double-Spiral Joint Tailored for Dexterous Hands
• Abstract summary: This paper presents a parametric doublespiral (PDS) compliant joint for large-stroke anthropomorphic motion.<n>It enables systematic shaping of directional stiffness across multiple deformation modes, including flexion/extension, abduction/adduction, and pronation/supination.<n>We co-design embedded inductive proprioception and propose a learningbased calibration pipeline that maps raw inductive signals to joint states.
• Score: 20.683032766025082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compliant joints can embed safety and adaptability into dexterous hands, but achieving large-stroke anthropomorphic motion while maintaining joint-specific, directiondependent stiffness and reliable proprioception remains challenging. This paper presents the PDS joint, a parametric doublespiral (PDS) compliant joint that enables systematic shaping of directional stiffness across multiple deformation modes, including flexion/extension, abduction/adduction, and pronation/supination. We instantiate the joint using Archimedean and logarithmic spiral templates for different hand joints and introduce an asymmetry ratio to tailor stiffness distributions for both grasp stability and hyperextension resistance. To make the joint practically usable under large deformation, we co-design embedded inductive proprioception and propose a learningbased calibration pipeline that maps raw inductive signals to joint states using ArUco-marker tracking. Experiments characterize the stiffness landscapes across geometric parameters and demonstrate a non-monotonic dependence of lateral support on asymmetry, indicating the importance of principled parameter tuning. For joint-state estimation in the most challenging abduction/adduction motion, a learned multilayer-perceptron (MLP) mapping reduces the error compared with conventional curve fitting by 41.6%. Finally, we integrate the proposed joints into an open-source dexterous hand as a demonstration platform, on which the hand grasps a set of nine everyday objects and performs safe, contact-rich human-involved interactions.

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