When Shall I Be Empathetic? The Utility of Empathetic Parameter Estimation in Multi-Agent Interactions
When Shall I Be Empathetic? The Utility of Empathetic Parameter Estimation in Multi-Agent Interactions
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DOI：
10.1109/icra48506.2021.9561079
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Yi Chen;Lei Zhang;Tanner Merry;Sunny Amatya;Wenlong Zhang;Yi Ren
中科院分区：
文献类型：
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作者：
Yi Chen;Lei Zhang;Tanner Merry;Sunny Amatya;Wenlong Zhang;Yi Ren
人机交互（HRI）可以建模为不完全信息的微分博弈，其中每个代理拥有私人奖励参数。由于在寻找这种游戏的完美贝叶斯均衡的公开挑战，现有的研究往往解耦的信念和物理动力学之间的迭代信念更新和运动规划。重要的是，机器人的奖励参数通常被假设为人类已知，以简化计算。在本文中，我们表明，在这种简化下，机器人对人类的参数进行非移情的信念更新，这会导致在不受控制的交叉口场景中的高安全风险。相反，我们提出了一个模型移情信念更新，其中代理更新的联合概率的所有代理的参数组合。更新使用神经网络，近似代理的纳什均衡动作值。我们比较移情和非移情的信念更新方法在两辆车的交叉口失控的情况下，反应时间短。结果表明，当两个代理人是不知不觉的侵略（或非侵略），移情是必要的，以避免冲突时，代理人有错误的相信对方的参数。本文论证了承认HRI的不完全信息性质的重要性。
Human-robot interactions (HRI) can be modeled as differential games with incomplete information, where each agent holds private reward parameters. Due to the open challenge in finding perfect Bayesian equilibria of such games, existing studies often decouple the belief and physical dynamics by iterating between belief update and motion planning. Importantly, the robot’s reward parameters are often assumed to be known to the humans, in order to simplify the computation. We show in this paper that under this simplification, the robot performs non-empathetic belief update about the humans’ parameters, which causes high safety risks in uncontrolled intersection scenarios. In contrast, we propose a model for empathetic belief update, where the agent updates the joint probabilities of all agents’ parameter combinations. The update uses a neural network that approximates the Nash equilibrial action-values of agents. We compare empathetic and non-empathetic belief update methods on a two-vehicle uncontrolled intersection case with short reaction time. Results show that when both agents are unknowingly aggressive (or non-aggressive), empathy is necessary for avoiding collisions when agents have false believes about each others’ parameters. This paper demonstrates the importance of acknowledging the incomplete-information nature of HRI.