To address this gap, we present a vision-language model (VLM) guided human-exoskeleton interface that translates natural user feedback and egocentric visual context into parameter updates for a hip exoskeleton controller. Our framework uses an off-the-shelf VLM that performs high-level intent parsing, while low-level action selection is offloaded to a contextual bandit. This bandit explores and exploits over two personalization parameters, the magnitude of assistance and the offset timing, while learning individual preferences over time. We use human feedback (Likert scale) ratings as rewards for the bandit, iterating parameter updates until the user reports satisfaction (Likert ≥ 5).

We evaluated this framework across a five-task locomotion track, spanning level ground, ramps, and stairs, across two laps, against a single-shot interpreter and a one-shot-feedback baseline. Our contextual bandit pipeline improved from default initializations within a single interaction, raising satisfaction by an average of 3 Likert points in multi-rating loops. No-improvement personalization instances were eliminated by the second lap, and user satisfaction interactions increased from 67% to 95% across laps. We demonstrate VLMs paired with contextual bandits as an effective framework for online, context-aware, user preference learning in real-time exoskeleton personalization.