  Customizing robotic behaviors to be aligned with diverse human preferences is
an underexplored challenge in the field of embodied AI. In this paper, we
present Promptable Behaviors, a novel framework that facilitates efficient
personalization of robotic agents to diverse human preferences in complex
environments. We use multi-objective reinforcement learning to train a single
policy adaptable to a broad spectrum of preferences. We introduce three
distinct methods to infer human preferences by leveraging different types of
interactions: (1) human demonstrations, (2) preference feedback on trajectory
comparisons, and (3) language instructions. We evaluate the proposed method in
personalized object-goal navigation and flee navigation tasks in ProcTHOR and
RoboTHOR, demonstrating the ability to prompt agent behaviors to satisfy human
preferences in various scenarios. Project page:
https://promptable-behaviors.github.io
