Promptable Behaviors: Personalizing Multi-Objective Rewards from Human
  Preferences
Minyoung Hwang Luca Weihs Chanwoo Park Kimin Lee Aniruddha Kembhavi Kiana Ehsani
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