NRI: FND: Robotic Collaboration through Scalable Reactive Synthesis

NRI：FND：通过可扩展反应合成进行机器人协作

• 批准号: 1830549
• 负责人: Lydia Kavraki
• 金额: $ 74.93万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830549&HistoricalAwards=false
• 关键词: NRI; FND; Robotic; Collaboration; through

As human-robot collaboration is scaled up to more and more complex tasks, there is an increased need for formally modeling the system formed by human and robotic agents. Such modeling enables reasoning about reliability, safety, correctness, and scalability of the system. The modeling, however, presents a daunting task. This research aspires to formally model scenarios where the robot and the human can have varying roles. The intent is to develop scalable methodologies that will endow the robot with the ability to adapt to human actions and preferences without changes to its underlying software or hardware. An assembly scenario will be used to mimic manufacturing settings where a robot and a human may work together and where the actions of the robot can improve the quality and safety of the work of the human. The project is a critical step towards making robots collaborative with and responsive to humans while allowing the human to be in control. This research will develop a framework for human-robot collaboration that integrates reactive synthesis from formal methods with robotic planning methods. By tightly combining the development of synthesis methods with robotics, it will pursue the development of a framework that is intuitive and scalable. The focus is on task-level collaboration as opposed to physical interaction with a human. The framework takes as input a task specification defined in a novel formal language interpreted over finite traces: a language suitable for robotics problems. It produces a policy for a robotic agent to assist a human agent regardless of which subtask or execution order for this subtask that the human agent chooses. The policy includes both high-level actions for the robotic agent as well as corresponding low-level motions that can be directly executed by the actual robot. One key novel component of the approach is the automated construction of abstractions for robotic manipulation that can be used by synthesis methods. The scalability of the proposed work will be investigated along different dimensions: the extent to which symbolic reasoning can be applied, the development of new synthesis algorithms, and the proper use of abstractions including their automatic refinement and the construction of factored abstractions. The trade-offs in using a combination of partial policies and replanning will be investigated as well as how to account for incomplete information due to incomplete observations. The theoretical contributions will be implemented on real robot hardware and demonstrated in experiments that are analogous to real-world assembly tasks.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.

随着人机协作扩展到越来越复杂的任务，越来越需要对人类和机器人代理形成的系统进行正式建模。这样的建模能够推理系统的可靠性、安全性、正确性和可扩展性。然而，建模是一项艰巨的任务。这项研究渴望正式建模的场景，机器人和人类可以有不同的角色。其目的是开发可扩展的方法，使机器人能够适应人类的行为和偏好，而无需更改其底层软件或硬件。装配场景将用于模拟制造环境，其中机器人和人类可以一起工作，并且机器人的动作可以提高人类工作的质量和安全性。该项目是使机器人与人类合作并对人类做出反应的关键一步，同时允许人类进行控制。这项研究将开发一个框架，人机协作，集成反应合成从正式的方法与机器人规划方法。通过将合成方法的开发与机器人技术紧密结合，它将追求开发一个直观和可扩展的框架。重点是任务级别的协作，而不是与人类的物理交互。该框架作为输入的任务规范定义在一个新的正式语言解释有限的痕迹：语言适合机器人问题。它产生一个机器人代理的政策，以协助人类代理，无论哪个子任务或执行顺序，这个子任务的人类代理选择。该策略包括机器人代理的高级动作以及可以由实际机器人直接执行的相应低级动作。该方法的一个关键的新的组成部分是机器人操作的抽象，可用于合成方法的自动化建设。拟议的工作的可扩展性将被调查沿着不同的维度：在何种程度上可以应用符号推理，新的合成算法的发展，并适当使用的抽象，包括其自动细化和建设的因素抽象。将研究使用部分政策和重新规划相结合的权衡，以及如何考虑由于不完整的观测结果而导致的不完整信息。理论贡献将在真实的机器人硬件上实现，并在类似于现实世界装配任务的实验中得到验证。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。

项目成果

Finite-Horizon Synthesis for Probabilistic Manipulation Domains

• DOI: 10.1109/icra48506.2021.9561297
• 发表时间: 2021-05
• 期刊: 2021 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Andrew M. Wells;Zachary K. Kingston;Morteza Lahijanian;L. Kavraki;Moshe Y. Vardi

LTLf Synthesis on Probabilistic Systems

• DOI: 10.4204/eptcs.326.11
• 发表时间: 2020-09
• 作者: Andrew M. Wells;Morteza Lahijanian;L. Kavraki;Moshe Y. Vardi

Extracting generalizable skills from a single plan execution using abstraction-critical state detection

使用抽象关键状态检测从单个计划执行中提取通用技能

• DOI: 10.1109/icra48891.2023.10161270
• 发表时间: 2023
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA
• 作者: Elimelech, Khen;Kavraki, Lydia E.;Vardi, Moshe Y.
• 通讯作者: Vardi, Moshe Y.

LTLf Synthesis as AND-OR Graph Search: Knowledge Compilation at Work

• DOI: 10.24963/ijcai.2022/359
• 发表时间: 2022-07
• 作者: G. D. Giacomo;Marco Favorito;Jianwen Li;M. Vardi;Shengping;Xiao;Shufang Zhu

Augmenting Control Policies with Motion Planning for Robust and Safe Multi-robot Navigation

通过运动规划增强控制策略，实现稳健、安全的多机器人导航

• DOI: 10.1109/iros45743.2020.9341153
• 发表时间: 2020
• 期刊: IEEE/RSJ International conference on Intelligent Robots and systems (IROS
• 作者: Pan, Tianyang;Verginis, Christos K.;Wells, Andrew M.;Kavraki, Lydia E.;Dimarogonas, Dimos V.
• 通讯作者: Dimarogonas, Dimos V.