ロボット行動に対する人間の選好をソフトな計画制約として学習
Learning Human Preferences Over Robot Behavior as Soft Planning Constraints
人間の選好をソフトな計画制約として表現し、ユーザーへの問い合わせからロボットが選好を推論するデータ駆動手法を提案し、Habitat 2.0の再配置タスクで有効性を示した。
著者: Austin Narcomey, Nathan Tsoi, Ruta Desai, Marynel Vázquez
分類: cs.RO
原文アブストラクト
Preference learning has long been studied in Human-Robot Interaction (HRI) in order to adapt robot behavior to specific user needs and desires. Typically, human preferences are modeled as a scalar function; however, such a formulation confounds critical considerations on how the robot should behave for a given task, with desired -- but not required -- robot behavior. In this work, we distinguish between such required and desired robot behavior by leveraging a planning framework. Specifically, we propose a novel problem formulation for preference learning in HRI where various types of human preferences are encoded as soft planning constraints. Then, we explore a data-driven method to enable a robot to infer preferences by querying users, which we instantiate in rearrangement tasks in the Habitat 2.0 simulator. We show that the proposed approach is promising at inferring three types of preferences even under varying levels of noise in simulated user choices between potential robot behaviors. Our contributions open up doors to adaptable planning-based robot behavior in the future.
