Skip to yearly menu bar Skip to main content


Poster

Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences

Minyoung Hwang · Luca Weihs · Chanwoo Park · Kimin Lee · Aniruddha Kembhavi · Kiana Ehsani

Arch 4A-E Poster #154
[ ] [ Project Page ] [ Paper PDF ]
[ Slides [ Poster
Thu 20 Jun 5 p.m. PDT — 6:30 p.m. PDT

Abstract:

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.

Chat is not available.