Tendon-based modelling, estimation and control for a simulated high-DoF anthropomorphic hand model

2026-01-28

AI Summary

This work addresses the challenge of achieving high-precision closed-loop control in tendon-driven anthropomorphic hands, which typically lack direct joint angle sensing. To circumvent the need for joint encoders, the authors propose a framework for joint angle estimation and control that combines a high-degree-of-freedom kinematic model—formulated using the Denavit–Hartenberg convention—with tendon displacement measurements and a simplified tension model to infer joint angles. A Jacobian-based PI controller augmented with feedforward compensation is then designed for accurate gesture tracking. Validated through nonlinear optimization and MuJoCo simulations on an Anatomically Correct Biomechatronic Hand, the approach successfully reproduces complex gestures with high fidelity, significantly enhancing control performance while preserving mechanical compactness.

Technology Category

• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Tendon-driven anthropomorphic robotic hands often lack direct joint angle sensing, as the integration of joint encoders can compromise mechanical compactness and dexterity. This paper presents a computational method for estimating joint positions from measured tendon displacements and tensions. An efficient kinematic modeling framework for anthropomorphic hands is first introduced based on the Denavit-Hartenberg convention. Using a simplified tendon model, a system of nonlinear equations relating tendon states to joint positions is derived and solved via a nonlinear optimization approach. The estimated joint angles are then employed for closed-loop control through a Jacobian-based proportional-integral (PI) controller augmented with a feedforward term, enabling gesture tracking without direct joint sensing. The effectiveness and limitations of the proposed estimation and control framework are demonstrated in the MuJoCo simulation environment using the Anatomically Correct Biomechatronic Hand, featuring five degrees of freedom for each long finger and six degrees of freedom for the thumb.

Problem

• tendon-driven
• joint angle estimation
• anthropomorphic hand
• sensorless control
• kinematic modeling

Innovation

• tendon-driven
• joint angle estimation
• nonlinear optimization
• Jacobian-based control
• anthropomorphic hand