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

2026-06-23

AI Summary

This work addresses the challenge of simultaneously achieving direction-dependent joint stiffness and high-precision proprioception in dexterous hands during large-range anthropomorphic motions. To this end, the authors propose a Parameterized Dual-Spiral (PDS) compliant joint that integrates Archimedean and logarithmic spiral templates to modulate multi-degree-of-freedom directional stiffness, augmented by an asymmetry ratio parameter to tailor stiffness distribution for both grasp stability and hyperextension resistance. For the first time, multimodal programmable stiffness and high-fidelity proprioception are unified through embedded inductive sensing, ArUco-based visual calibration, and an MLP-based learning mapping. The approach reduces state estimation error by 41.6% in abduction/adduction movements compared to conventional methods. An open-source dexterous hand equipped with PDS joints successfully grasps nine categories of everyday objects, demonstrating its capability for safe human–robot interaction.

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.

Problem

• compliant joints
• directional stiffness
• proprioception
• dexterous hands
• anthropomorphic motion

Innovation

• compliant joint
• directional stiffness
• inductive proprioception
• parameterized spiral design
• learning-based calibration