Trust-Aware Decision Making for Human-Robot Collaboration
Trust-Aware Decision Making for Human-Robot Collaboration
复制标题
人机协作的信任感知决策
DOI：
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复制
发表时间：
2018
影响因子：
5.1
通讯作者：
S. Srinivasa
中科院分区：
文献类型：
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作者：
Min Chen;S. Nikolaidis;Harold Soh;David Hsu;S. Srinivasa
对自主性的信任对于有效的人机协作和用户采用机器人助理等自主系统至关重要。本文介绍了一个将信任集成到机器人决策中的计算模型。具体来说，我们从数据中学习到一个部分可观察的马尔可夫决策过程（POMDP），其中人类信任是一个潜在变量。trust- pomdp模型为机器人(i)通过互动推断人类队友的信任，（ii）推理其自身行为对人类信任的影响，以及（iii）选择长期最大化团队绩效的行为提供了一种原则性方法。我们通过模拟（201名参与者）和真实机器人（20名参与者）的清理桌子任务的人类受试者实验来验证该模型。在我们的研究中，机器人首先通过操纵低风险的物体来建立人类的信任。有趣的是，机器人有时会故意无法调节人类的信任，实现最佳的团队表现。这些结果表明，信任- pomdp在长期内校准信任以提高人-机器人团队绩效。此外，他们还强调，仅仅最大化信任并不总能带来最佳绩效。
Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This article introduces a computational model that integrates trust into robot decision making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term. We validated the model through human subject experiments on a table clearing task in simulation (201 participants) and with a real robot (20 participants). In our studies, the robot builds human trust by manipulating low-risk objects first. Interestingly, the robot sometimes fails intentionally to modulate human trust and achieve the best team performance. These results show that the trust-POMDP calibrates trust to improve human-robot team performance over the long term. Further, they highlight that maximizing trust alone does not always lead to the best performance.
影响因子：
5.8
作者：
Carpenter, Bob;Gelman, Andrew;Riddell, Allen
通讯作者：
Riddell, Allen