Delay-Compensated Stiffness Estimation for Robot-Mediated Dyadic Interaction

TL;DR

This paper introduces a delay-compensated stiffness estimation method for robot-mediated dyadic interactions, improving accuracy under network delays to enhance remote physical therapy and haptic perception.

Contribution

It presents a novel algebraic estimator that explicitly accounts for delays and a robust filtering approach, outperforming standard methods in delayed environments.

Findings

Significantly reduces estimation errors under network delays

Maintains consistent tracking accuracy with multiple delay scenarios

Demonstrates effectiveness on commercial rehabilitation robots

Abstract

Robot-mediated human-human (dyadic) interactions enable therapists to provide physical therapy remotely, yet an accurate perception of patient stiffness remains challenging due to network-induced haptic delays. Conventional stiffness estimation methods, which neglect delay, suffer from temporal misalignment between force and position signals, leading to significant estimation errors as delays increase. To address this, we propose a robust, delay-compensated stiffness estimation framework by deriving an algebraic estimator based on quasi-static equilibrium that explicitly accounts for temporally aligning the expert's input with the novice's response. A Normalised Weighted Least Squares (NWLS) implementation is then introduced to robustly filter dynamic bias resulting from the algebraic derivation. Experiments using commercial rehabilitation robots (H-MAN) as the platform demonstrate that the proposed method significantly outperforms the standard estimator, maintaining consistent tracking accuracy under multiple introduced delays. These findings offer a promising solution for achieving high-fidelity haptic perception in remote dyadic interaction, potentially facilitating reliable stiffness assessment in therapeutic settings across networks.