NRI: FND: COLLAB: Coordinating Human-Robot Teams in Uncertain Environments

机器人硬件的成本下降和复杂性的提高为机器人团队与熟练的人类结合部署创造了新的机会，以支持和增加劳动密集型和/或危险的手工工作。他们的愿景是让机器人解放技术工人的时间，这样他们就可以专注于他们擅长的任务（解决复杂的问题、灵巧的操作、客户服务等），机器人可以帮助解决工作中分散注意力和令人沮丧的部分，比如运送材料或提取物资。这一愿景正在美国经济和国外的许多部门实现，例如仓库管理、装配制造和灾难响应。然而，这一领域的进展正受到当前方法的阻碍，这些方法既僵化又不灵活，而且依赖于不切实际的人机交互模型。这个项目试图通过提出新的模型和方法来克服这些问题，让团队机器人与团队人类协调完成复杂的问题。特别是，该项目将创建并解决在不确定环境中协调人类和机器人团队的现实模型。pi将研究这一研究领域的创新方法，并将做出以下贡献：1)实现多人多机器人团队合作的转型重新概念化，准确反映团队的优势和局限性，就像在一个暂时动态的随机环境中一样；2)开发考虑不确定性和部分可观察性的现实和一般的人机团队合作模型；3)为这些模型中的规划和学习提供创新和可扩展的技术。本研究将建立在不确定性和部分可观察性下的单机器人问题成功的方法：部分可观察马尔可夫决策过程（pomdp）。pomdp可以模拟机器人和环境，但不能模拟人类。然而，在几乎所有现实世界的应用程序中，显式地将人包括在这些模型中是至关重要的。通过将pomdp扩展到与人类团队交互的多个机器人，可以表示人类和机器人混合团队的复杂和现实问题。在这个项目中开发的解决方案方法将允许机器人推理关于领域和他们的人类队友的不确定性，同时优化他们的行为。这些方法广泛适用于人机协作领域，但它们将在急诊科进行评估，这是一个具有大量不确定性的环境，并且在大容量时期有许多交付和供应任务。一组机器人可以协助完成这些任务。实验将在模拟和加州大学圣地亚哥分校模拟和训练中心进行，有不同数量的人和机器人。这个项目的结果有可能改变人与机器人协调的方式。

The decreasing cost and increasing sophistication of robot hardware is creating new opportunities for teams of robots to be deployed in combination with skilled humans to support and augment labor-intensive and/or dangerous manual work. The vision is for robots to free up time of skilled workers so they can focus on the tasks that they are skilled at (complex problem solving, dextrous manipulation, customer service, etc.) and robots can help with the distracting and frustrating parts of working, such as delivering materials or fetching supplies. This vision is being realized across many sectors of the US economy and abroad, such as in warehouse management, assembly manufacturing, and disaster response. However, progress in this area is being stymied by current methods that are rigid and inflexible, and rely on unrealistic models of human-robot interaction. This project seeks to overcome these problems by proposing new models and methods for teams robots to coordinate with teams humans to complete complex problems. In particular, this project will create and solve realistic models for coordinating teams of humans and robots in uncertain environments. The PIs will investigate innovative approaches to this research area, and will make the following contributions: 1) Enable a transformative re-conceptualization of multi-human multi-robot teamwork the accurately reflects the strengths and limitations of the team, as situated within a temporally dynamic, stochastic environment, 2) develop realistic and general models of human-robot teamwork that consider uncertainty and partial observability, and 3) Contribute innovative and scalable techniques for planning and learning in these models. This research will build off of methods that have been successful in single-robot problems under uncertainty and partially observability: partially observable Markov decision processes (POMDPs). POMDPs model robots and environments, but not humans. However, explicitly including people in these models will be critical in almost all real-world applications. By extending POMDPs to multiple robots interacting with teams of humans, complex and realistic problems with mixed human and robot teams can be represented. The solution methods developed in this project will allow the robots to reason about the uncertainty about the domain and their human teammates, while optimizing their behavior. The methods are broadly applicable to human-robot collaboration domains, but they will be evaluated in an emergency department, an environment with a large amount of uncertainty and many delivery and supply tasks during high-volume times. A team of robots can assist in these tasks. Experiments will take place in simulation and in the UC San Diego Simulation and Training Center with various numbers of humans and robots. The results of this project have the potential to transform the way human-robot coordination is performed.