NRI: FND: Coordinating and Incorporating Trust in Teams of Humans and Robots with Multi-Robot Reinforcement Learning

NRI：FND：通过多机器人强化学习协调和整合人类和机器人团队的信任

基本信息

批准号：
2024790
负责人：
Christopher Amato
金额：
$ 64.7万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024790&HistoricalAwards=false
关键词：
NRI FND Coordinating Incorporating Trust

项目摘要

The decreasing cost and increasing sophistication of robot hardware has created new opportunities for applications where teams of robots can be deployed in combination with skilled humans to automate labor-intensive tasks. However, if such systems are to become widely deployable, they must be able to appropriately reason about human teamwork. Therefore, this project will create new methods for generating and solving models for teams of multiple humans and robots working together to solve complex problems. These approaches will be able to learn quickly from limited interactions and consider the dynamic and uncertain nature of coordinating teams of robots and humans. Furthermore, the project will develop methods that allows for communication between the robots and humans and incorporates models of trust to permit humans and robots to appropriately establish trust in each other.In particular, this project will produce several novel methods for modeling and learning solutions for teams of robots interacting with multiple people. The approaches will leverage the strengths of POMDPs to consider the dynamic and uncertain nature of coordinating teams of robots and humans. Because sample efficiency is of utmost importance when dealing with humans and real-world tasks when the number of interactions will be limited, the project will develop Bayesian reinforcement learning methods that scale by exploiting hierarchy and deep learning. The project will also develop methods for communication and shared mental models to allow the humans and robots to have confidence in what each other is doing. These methods will allow tight cooperation between the humans and robots. Furthermore, humans will not want to use our system if they cannot trust the robots. Therefore, the project will develop methods that model and incorporate trust into the approach while generating interpretable POMDP models and solutions that can be shared with humans during or after execution. These advances will produce high-quality solutions for mixed human-robot teams in realistic scenarios.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

机器人硬件成本的下降和复杂性的提高为应用创造了新的机会，在这些应用中，可以将机器人团队与熟练的人类结合部署，以实现劳动密集型任务的自动化。然而，如果此类系统要广泛部署，它们必须能够适当地推理人类团队合作。因此，该项目将创建新的方法来生成和解决多个人类和机器人团队共同解决复杂问题的模型。这些方法将能够从有限的交互中快速学习，并考虑机器人和人类协调团队的动态和不确定性。此外，该项目将开发允许机器人和人类之间进行通信的方法，并结合信任模型，以允许人类和机器人适当地相互建立信任。特别是，该项目将产生几种用于建模和学习解决方案的新颖方法与多人互动的机器人团队。这些方法将利用 POMDP 的优势来考虑机器人和人类协调团队的动态和不确定性。由于在交互数量有限的情况下处理人类和现实世界任务时，样本效率至关重要，因此该项目将开发通过利用层次结构和深度学习进行扩展的贝叶斯强化学习方法。该项目还将开发沟通方法和共享心理模型，让人类和机器人对彼此所做的事情充满信心。这些方法将使人类和机器人之间能够紧密合作。此外，如果人类不能信任机器人，他们就不会想使用我们的系统。因此，该项目将开发对信任进行建模并将信任融入该方法的方法，同时生成可解释的 POMDP 模型和解决方案，可以在执行期间或执行后与人类共享。这些进步将为现实场景中的混合人机团队提供高质量的解决方案。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。