让机器人引导人类到关键决策点，更快猜出对方目标。
Robots Influencing Humans to Reveal their Goals during Collaboration and Competition
- 通过设计关键决策点引导人类行为
- 在模拟和真实机器人上提前准确推断目标
- 适用于协作与对抗场景的快速目标推理
我们提出一种统一策略，实现人机交互中的快速目标推断。核心思想是将人类引导至关键决策点（CDPs）——即不同人类策略在此处会做出不同动作的状态，从而最大化暴露其目标。通过目标条件下的策略分歧度定义CDPs，并将其融入滚动时域规划器，该规划器在优化任务进展与信息增益的联合成本函数时，探索未来动作序列。我们在仿真和真实机器人上评估了该方法，在协作的烹饪任务和对抗的藏匿-搜寻游戏中均表现更优：相比基线策略，本方法能更早、更准确地推断出人类目标。
原文摘要 · Abstract (English)
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.
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