iProDMP: An Enhanced Probabilistic Dynamic Movement Primitives Framework for Hip Exoskeleton-Assisted Lifting

This study introduces iProDMP, a novel framework that integrates human behavioral preferences into exoskeleton learning through unified probabilistic movement modeling. The framework addresses three critical limitations: Dynamic movement primitives (DMPs)’ inability to represent preference uncertainty, inadequate trajectory generation beyond observation range in probabilistic movement primitives (ProMPs), and unreliable via-point modulation in hybrid approaches. Our solution features three innovations: First, we establish dynamic-probabilistic consistency conditions for unified DMP-ProMP frameworks, which enable stochastic modeling of human preferences while preserving attractor stability. Second, a novel scaling method decouples shape modulation from model hyperparameters, enabling flexible motion adaptation. Third, consistency-guaranteed expectation-maximization resolves parameter optimization within the unified framework. Experiments on lifting trajectory imitation demonstrate strong extrapolation beyond demonstration distributions, particularly for via-points outside training data, thereby validating adaptability to variable task conditions. In hip exoskeleton-assisted lifting tasks, our approach achieves a 28% improvement in assistance efficiency over conventional implementations.