HCC: Small: Modeling Ad Hoc Collaboration on Complex Manipulation Tasks for Human-Robot Teams

该项目将研究人们如何相互协作以完成复杂的物理任务，并研究如何使机器人与人在类似任务中合作。许多任务协作都是临时性的，随着任务的展开，团队成员决定谁来完成任务的哪些部分。在人机团队中实现类似的紧急协作对于家庭援助、柔性制造和搜索救援等应用至关重要。该项目将有助于理解复杂的人类团队合作以及实现有效的人类-机器人团队所需的基本技术。这项研究将被广泛传播，并通过指导具有不同背景的学生团队来扩大对计算的参与，以解决该项目的人力和技术方面的问题。为了实现这些目标，该项目将调查复杂操作任务的临时合作。虽然多年来对人机协作的研究已经扩大，但先前的工作在很大程度上限于小规模任务的研究（例如，桌面操作），二元团队合作（即，两人团队），和/或规定的协作（例如，具有预定角色和程序的任务）。通过这个项目，研究小组将1）研究一个原则性的框架，用于描述，表示，推理，并基于对人类在复杂操作任务上的自组织协作的严格建模来实现协作交互; 2）开发使用感知数据推断和接地协作事件和行动的计算方法;以及3）探索如何利用所开发的框架和推理能力来使特设的人-该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的学术价值和更广泛的影响审查标准。

This project will study how people collaborate with each other to complete complex physical tasks and investigate ways to enable robots to collaborate with people in similar tasks. Many task collaborations emerge on an ad hoc basis, with teammates making decisions about who does what parts of the task as it unfolds. Achieving similar emergent collaboration in human-robot teamwork is critical for applications such as in-home assistance, flexible manufacturing, and search-and-rescue. This project will contribute to the understanding of complex human teamwork and the fundamental technologies needed to bring about effective human-robot teams. This research will be widely disseminated, and will broaden participation in computing by mentoring teams of students with the diverse backgrounds needed to address both the human and the technical aspects of the project.To achieve these goals, this project will investigate ad hoc collaboration on complex manipulation tasks. While research on human-robot collaboration has expanded over the years, prior work has been largely limited to the investigation of small-scale tasks (e.g., tabletop manipulation), dyadic teamwork (i.e., two-person teams), and/or prescribed collaboration (e.g., tasks with predetermined roles and procedures). Through this project, the research team will 1) investigate a principled framework for describing, representing, reasoning, and enabling collaborative interactions based on rigorous modeling of human ad hoc collaboration on complex manipulation tasks; 2) develop computational methods for inferring and grounding collaborative events and actions using perception data; and 3) explore how the developed framework and reasoning capabilities may be leveraged to enable ad hoc human-robot collaboration in an experimentally constrained setting.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.