協調と競争におけるロボットによる人間の目標顕在化の促進
Robots Influencing Humans to Reveal their Goals during Collaboration and Competition
人間とロボットの相互作用において、人間を臨界決定点へ導くことで目標推論を高速化する統一戦略を提案し、協調的調理タスクと競争的かくれんぼゲームで評価した。
著者: Debasmita Ghose, Oz Gitelson, Michal Lewkowicz, Jake Brawer, Marynel Vazquez, Brian Scassellati
分類: cs.RO, cs.AI
原文アブストラクト
We propose a unified strategy for fast goal inference in human-robot interaction. The core idea is to drive the human toward Critical Decision Points (CDPs)-states where competing human strategies prescribe different next actions and thus maximally reveal the goal. We formalise CDPs using a goal-conditioned policy divergence measure and incorporate them into a Receding-Horizon Planner that explores future action sequences while optimizing a cost function balancing task progress and information gain. We evaluate this approach in both a collaborative, fully observable cooking task and a competitive, partially observable hide-and-seek game, each in simulation and on real robots. In both scenarios, our method infers human goals more accurately and earlier than baseline strategies.