Skill-informed Data-driven Haptic Nudges for High-dimensional Human Motor Learning

TL;DR

This paper introduces a data-driven framework using haptic feedback and probabilistic modeling to enhance high-dimensional human motor learning, validated through human trials with improved performance.

Contribution

It presents a novel POMDP-based approach for designing optimal haptic nudges that adapt to learner skill, advancing motor learning in complex tasks.

Findings

Participants with POMDP-guided nudges learned faster and more accurately.

The approach accelerates discovery of efficient motor representations.

Participants showed improved movement efficiency and endpoint accuracy.

Abstract

In this work, we propose a data-driven framework to design optimal haptic nudge feedback leveraging the learner's estimated skill to address the challenge of learning a novel motor task in a high-dimensional, redundant motor space. A nudge is a series of vibrotactile feedback delivered to the learner to encourage motor movements that aid in task completion. We first model the stochastic dynamics of human motor learning under haptic nudges using an Input-Output Hidden Markov Model (IOHMM), which explicitly decouples latent skill evolution from observable performance measures. Leveraging this predictive model, we formulate the haptic nudge feedback design problem as a Partially Observable Markov Decision Process (POMDP). This allows us to derive an optimal nudging policy that minimizes long-term performance cost and implicitly guides the learner toward superior skill states. We validate our approach through a human participant study (N=30) involving a high-dimensional motor task rendered through a hand exoskeleton. Results demonstrate that participants trained with the POMDP-derived policy exhibit significantly accelerated movement efficiency and endpoint accuracy compared to groups receiving heuristic-based feedback or no feedback. Furthermore, synergy analysis reveals that the POMDP group discovers efficient low-dimensional motor representations more rapidly.