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

NRI：FND：COLLAB：在不确定环境中协调人机团队

• 批准号: 1734482
• 负责人: Laurel Riek
• 金额: $ 37.5万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734482&HistoricalAwards=false
• 关键词: NRI; FND; COLLAB; Coordinating; Human

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.

机器人硬件的成本下降和成熟的成本正在为机器人团队与熟练的人类联合使用，以支持和增强劳动密集型和/或危险的手动工作。愿景是让机器人释放熟练工人的时间，以便他们可以专注于他们熟练的任务（复杂的问题，敏捷的操纵，客户服务等），机器人可以帮助分散工作和令人沮丧的工作部分，例如提供材料或提供材料或提取物品。在美国经济和国外的许多部门，例如在仓库管理，组装制造和灾难响应中，都在实现这种愿景。但是，这一领域的进展受到当前刚性和僵化的方法的困扰，并依赖于人类机器人相互作用的不切实际模型。该项目旨在通过提出新的模型和方法来克服这些问题，以使团队机器人与团队人工协调以完成复杂问题。特别是，该项目将创建并解决现实的模型，以协调不确定环境中的人类和机器人团队。 PI将调查该研究领域的创新方法，并将做出以下贡献：1）对多人类多机器人团队合作进行变革性重新构化，准确地反映团队的优势和局限性，因为在时间动态的，随机的，随机性的环境中，并确定了不可思议的团队，并构成了人类的宣传和一般性的延伸，并分别遵守人类运动，并部分地遵守人类运动，并分配一般性的模型这些模型中计划和学习的技术。这项研究将建立在不确定性和部分可观察性下在单机器人问题中成功的方法：部分可观察到的马尔可夫决策过程（POMDPS）。 POMDPS模型机器人和环境，而不是人类。但是，在几乎所有现实世界中，这些模型中的人员都至关重要。 通过将POMDP扩展到与人类团队相互作用的多个机器人，可以代表混合人和机器人团队的复杂和现实问题。该项目中开发的解决方案方法将使机器人能够在优化其行为的同时推荐有关该领域及其人类队友的不确定性。这些方法广泛适用于人类机器人协作领域，但将在急诊室进行评估，在高量期间，具有大量不确定性以及许多交付和供应任务的环境。一组机器人可以协助这些任务。实验将在模拟和加州大学圣地亚哥分校模拟和培训中心进行各种人类和机器人进行。该项目的结果有可能改变人类机器人协调的方式。

项目成果

REGROUP: A Robot-Centric Group Detection and Tracking System

• DOI: 10.1109/hri53351.2022.9889634
• 发表时间: 2022-03
• 期刊: 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
• 作者: Angelique Taylor;L. Riek

Fluent Coordination in Proximate Human Robot Teaming

• 发表时间: 2019
• 作者: S. Matsumoto;L. Riek

Robot-Centric Perception of Human Groups

• DOI: 10.1145/3375798
• 发表时间: 2020-05
• 期刊: ACM Transactions on Human-Robot Interaction (THRI)
• 作者: Angelique Taylor;Darren M. Chan;L. Riek

Situating Robots in the Emergency Department

• 发表时间: 2020
• 作者: Angelique Taylor;S. Matsumoto;L. Riek

Unseen Salient Object Discovery for Monocular Robot Vision

• DOI: 10.1109/lra.2020.2968059
• 发表时间: 2020-04
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Darren M. Chan;L. Riek