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.

机器人硬件的成本下降和成熟的成熟度为应用程序创造了新的机会，在这些应用程序中，机器人团队可以与熟练的人组合部署，以使劳动密集型任务自动化。但是，如果要广泛部署此类系统，他们必须能够适当地理解人类团队合作。因此，该项目将创建新的方法，用于为多个人类和机器人团队共同解决复杂问题的团队。这些方法将能够从有限的互动中快速学习，并考虑机器人和人类协调团队的动态和不确定性质。此外，该项目将开发允许机器人与人类之间进行交流的方法，并结合信任模型，以允许人类和机器人彼此适当地建立信任。尤其是，该项目将为与多人互动的机器人团队建模和学习解决方案提供多种新颖方法。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质。由于样本效率在处理人类和现实世界任务时至关重要，当交互的数量受到限制时，该项目将开发贝叶斯强化学习方法，通过利用层次结构和深度学习来扩展。该项目还将开发用于交流和共享心理模型的方法，以使人类和机器人对彼此的工作有信心。这些方法将允许人与机器人之间的紧密合作。此外，如果人类不能信任机器人，他们将不想使用我们的系统。因此，该项目将开发建模并将信任纳入该方法的方法，同时生成可解释的POMDP模型和解决方案，这些模型和解决方案可以在执行期间或之后与人类共享。这些进步将在现实情况下为混合的人类机器人团队提供高质量的解决方案。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准通过评估来支持的。