Symmetry-Aware Skill Transfer with Energy-Tank Passive Control for Ankle Exoskeletons

Abstract

This paper presents a unified framework that combines symmetry-aware skill transfer with energy-tank passive control to achieve safe and adaptive ankle exoskeleton assistance. Subject-specific ankle references are first extracted from wearable IMU data : Dynamic Time Warping (DTW) aligns gait cycles onto a normalized phase axis , and Gaussian Mixture Regression (GMR) synthesizes smooth probabilistic templates suitable for online modulation. When only unilateral sensing is available, contralateral trajectories are reconstructed through either a half-period phase shift or a DTW-informed nonlinear mapping, enabling robust bilateral assistance. These references are then tracked by a joint-space PID controller wrapped with an energy tank, which bounds power exchange and prevents unintended energy injection. In simulation experiments, the proposed controller improved center-of-mass smoothness relative to plain PID. Benchtop validation confirms the efficacy of both GMR-generated and symmetric-generated trajectories. Furthermore, experimental results show a reduction of 40 N in peak interaction force (from 120 N to 80 N), resulting in less mechanical strain on the user. By unifying phase-consistent gait synthesis with passivity shaping, this work advances ankle exoskeleton assistance that is individualized, robust, and inherently safe.