CPS:Medium:Collaborative Research: Safe Learning in Co-robots--Theory, Experiments and Education

CPS：中：协作研究：协作机器人的安全学习——理论、实验和教育

• 批准号: 1931853
• 负责人: Francesco Borrelli
• 金额: $ 120万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931853&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Safe

This award will support fundamental research on scalable and safe collaborative cyber physical systems. Specifically, this project will address the question of how to balance adaptability with safety for large human-robot teams. While pre-programmed robots can perform well in a perfectly known and unchanging workspace, accomplishing complex tasks under real-world conditions requires the ability to adapt to unexpected circumstances. This adaptability can be provided using artificial intelligence or machine learning techniques, but the resulting robot behavior becomes less predictable, which in turn makes it difficult to guarantee safe human-robot interactions. This project addresses the challenge of guaranteed safety by using an innovative integration of machine learning with techniques from control theory and nonlinear dynamics. In contrast to current approaches, the methods studied here will be scalable, that is, they will remain practical to implement even when the number of interacting humans and robots become large. Many important applications can benefit from the results of this project, including disaster relief, rescue missions, homeland security, and assisted healthcare. This project will also develop a teaching platform for collaborative human-robot engineering that will be used to teach collaborative robotics in large classes, and to help broaden the participation of underrepresented groups in research.Robots that collaborate with human partners in a shared physical workspace are called co-robots or cobots. This research will address the fundamental challenge of safety and performance guarantees as collaborative human-robot cyber physical systems move from model-driven control approaches to data-driven methods. In particular, the project will focus on data-rich iterative tasks performed by groups of humans and robots, and dynamically challenging tasks where human-robot and robot-robot interaction forces are complex to model. Statistical learning theory will be merged with predictive control theory using a mix of physics-based and data-driven models in the learning process. In the co-robot cyber physical systems under study, robot and human models will be updated in real-time using data feeds. Within each robot such models will be used by a predictive controller to forecast robot motion and human interaction, and to take corresponding safe and collaborative actions. The new theory resulting from this project will provide statistically rigorous guarantees of performance improvement and safety during the learning process.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.

该奖项将支持可扩展和安全的协作网络物理系统的基础研究。具体地说，这个项目将解决如何平衡大型人类-机器人团队的适应性和安全性的问题。虽然预先编程的机器人可以在完全已知和不变的工作空间中很好地执行任务，但在现实世界条件下完成复杂任务需要适应意外环境的能力。这种适应性可以使用人工智能或机器学习技术来提供，但由此产生的机器人行为变得更难预测，这反过来又使得难以保证人与机器人的安全交互。这个项目通过使用机器学习与控制理论和非线性动力学技术的创新集成来解决保证安全的挑战。与目前的方法相比，这里研究的方法将是可扩展的，也就是说，即使在交互的人类和机器人的数量变得很大的情况下，它们仍然可以实际实施。许多重要的应用可以从该项目的成果中受益，包括救灾、救援任务、国土安全和辅助医疗。该项目还将开发一个协作人-机器人工程教学平台，用于在大班中教授协作机器人学，并帮助扩大未被充分代表的群体参与研究的范围。在共享的物理工作空间中与人类合作伙伴协作的机器人称为协作式机器人或Cobots。随着协作人-机器人网络物理系统从模型驱动的控制方法向数据驱动的方法转变，这项研究将解决安全和性能保证的根本挑战。特别是，该项目将专注于由人类和机器人群体执行的数据丰富的迭代任务，以及人-机器人和机器人-机器人相互作用力难以建模的动态挑战任务。统计学习理论将与预测控制理论相结合，在学习过程中混合使用基于物理和数据驱动的模型。在正在研究的联合机器人网络物理系统中，机器人和人类模型将使用数据馈送实时更新。在每个机器人内部，预测控制器将使用这些模型来预测机器人的运动和人类交互，并采取相应的安全和协作行动。这个项目产生的新理论将为学习过程中的表现改进和安全提供统计上的严格保证。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Robust Control Barrier–Value Functions for Safety-Critical Control

用于安全关键控制的鲁棒控制屏障值函数

• DOI: 10.1109/cdc45484.2021.9683085
• 发表时间: 2021
• 期刊: IEEE Conference on Decision and Control (CDC
• 作者: Choi, Jason J.;Lee, Donggun;Sreenath, Koushil;Tomlin, Claire J.;Herbert, Sylvia L.
• 通讯作者: Herbert, Sylvia L.

Data-Driven Hierarchical Predictive Learning in Unknown Environments

未知环境中数据驱动的分层预测学习

• DOI: 10.1109/case48305.2020.9216872
• 发表时间: 2020
• 期刊: IEEE International Conference on Automation Science and Engineering CASE
• 作者: Vallon, Charlott;Borrelli, Francesco
• 通讯作者: Borrelli, Francesco

Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty

面向具有驱动不确定性的非线性系统的鲁棒数据驱动控制综合

• DOI: 10.1109/cdc45484.2021.9683511
• 发表时间: 2021
• 期刊: 2021 60th IEEE Conference on Decision and Control (CDC
• 作者: Taylor, Andrew J.;Dorobantu, Victor D.;Dean, Sarah;Recht, Benjamin;Yue, Yisong;Ames, Aaron D.
• 通讯作者: Ames, Aaron D.

Distributed Learning Model Predictive Control for Linear Systems

线性系统的分布式学习模型预测控制

• DOI: 10.1109/cdc42340.2020.9303820
• 发表时间: 2020
• 期刊: 2020 59th IEEE Conference on Decision and Control (CDC
• 作者: Sturz, Yvonne R.;Zhu, Edward L.;Rosolia, Ugo;Johansson, Karl H.;Borrelli, Francesco
• 通讯作者: Borrelli, Francesco

Collaborative Navigation and Manipulation of a Cable-Towed Load by Multiple Quadrupedal Robots

多个四足机器人对缆索牵引负载的协作导航和操纵

• DOI: 10.1109/lra.2022.3191170
• 发表时间: 2022
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Yang, Chenyu;Sue, Guo Ning;Li, Zhongyu;Yang, Lizhi;Shen, Haotian;Chi, Yufeng;Rai, Akshara;Zeng, Jun;Sreenath, Koushil
• 通讯作者: Sreenath, Koushil