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
@article{arxiv.2312.09337,
title = {Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences},
author = {Minyoung Hwang and Luca Weihs and Chanwoo Park and Kimin Lee and Aniruddha Kembhavi and Kiana Ehsani},
journal= {arXiv preprint arXiv:2312.09337},
year = {2023}
}