H-PAC Hand: Control-Oriented Modeling and Tendon-Elasticity Compensation for an Underactuated Robotic Hand

2026-08-17

AI Summary

This study addresses the low pose reproduction accuracy in underactuated tendon-driven hands caused by tendon elasticity. We propose the H-PAC modular robotic hand and a hierarchical control framework to overcome this limitation. By integrating a control-oriented sparse analytical model with a mechanical elasticity compensation mechanism and ESP32-based synchronous servo control, sensorless high-precision pose regulation is achieved. This approach effectively suppresses joint errors without requiring task-specific retuning. Experimental results demonstrate that the mean absolute error (MAE) of joint angle prediction remains below 0.23°, while the index finger DIP joint error is significantly reduced from 1.15° to 0.18°. These findings validate the proposed method’s effectiveness in achieving precision control for underactuated systems.

Abstract

Underactuated tendon-driven hands offer compact actuation and passive compliance, but tendon elongation under restoring-spring loading introduces configuration-dependent joint deviations. This paper presents H-PAC, a modular 6-actuator, 15-DoF robotic hand with a control-oriented modeling and implementation framework. A sparse analytical actuator-joint model is derived from the tendon-routing geometry, and a mechanics-based compensation model is developed to account for tendon-elasticity-induced joint errors. The proposed method is implemented in a hierarchical architecture: a host computer performs workspace-constrained posture mapping and compensation, while an ESP32 generates synchronized commands for six position-controlled servos. The same control parameters and execution strategy are used across all tasks without task-specific retuning. Monotonic servo-sweep experiments show that the compensation substantially improves joint-angle prediction. The MAE of the index DIP joint decreases from 1.15 degrees to 0.18 degrees, and all nine evaluated joints achieve an MAE below 0.23 degrees. Representative postures and grasping configurations are further executed using the same control pipeline without external joint or force sensing in the control loop. The results demonstrate a practical approach to improving posture reproducibility in compact underactuated robotic end-effectors.

Problem

• Underactuated robotic hand
• Tendon elasticity
• Joint deviation
• Posture reproducibility
• Control-oriented modeling

Innovation

• Tendon-Elasticity Compensation
• Control-Oriented Modeling
• Underactuated Robotic Hand
• Sparse Analytical Model
• Hierarchical Control Architecture