  Preference-aligned robot navigation in human environments is typically
achieved through learning-based approaches, utilizing demonstrations and user
feedback for personalization. However, personal preferences are subject to
change and might even be context-dependent. Yet traditional reinforcement
learning (RL) approaches with a static reward function often fall short in
adapting to these varying user preferences. This paper introduces a framework
that combines multi-objective reinforcement learning (MORL) with
demonstration-based learning. Our approach allows for dynamic adaptation to
changing user preferences without retraining. Through rigorous evaluations,
including sim-to-real and robot-to-robot transfers, we demonstrate our
framework's capability to reflect user preferences accurately while achieving
high navigational performance in terms of collision avoidance and goal
pursuance.
