Automated Curriculum Design for High-dimensional Human Motor Learning

2026-05-14

AI Summary

This study addresses the challenge in high-dimensional motor skill acquisition where true skill states are unobservable and task performance poorly reflects underlying learning progress, thereby hindering effective practice design. To overcome this limitation, the authors propose an automated curriculum generation framework that integrates a human motor learning model, personalized real-time skill estimation, and stochastic nonlinear model predictive control (SNMPC), enabling model-based dynamic curriculum optimization for the first time. Evaluated in both simulation and a hand exoskeleton experiment involving 36 participants, the proposed approach significantly accelerates skill acquisition—improving learning speed by approximately 23% over random curricula and 17% over performance-heuristic baselines—demonstrating its efficacy and novelty.

Abstract

Designing effective practice schedules for high-dimensional motor learning tasks remains a challenge, especially when skill states are unobservable and task performance may not reflect the true learning. We propose an automated curriculum design framework that combines a human motor learning model and personalized real-time skill estimation with Stochastic Nonlinear Model Predictive Control in \emph{de-novo} (novel) motor learning paradigms. We validated our framework both through simulations and human-subject studies (N = 36) using a hand exoskeleton. Our proposed approach accelerates skill acquisition by $\sim23\%$, and ${\sim17\%}$ when compared to a random curriculum and a performance heuristics-based curriculum, respectively. These significant gains in learning efficiency highlight the potential of model-based, individualized curricula for motor rehabilitation and complex skill training.

Problem

• Curriculum Design
• Motor Learning
• High-dimensional Tasks
• Skill Acquisition
• Human Motor Control

Innovation

• Automated Curriculum Design
• Human Motor Learning
• Stochastic Nonlinear Model Predictive Control
• Real-time Skill Estimation
• De-novo Motor Learning