FRR: Collaborative Research: Collaborative Learning for Multi-robot Systems with Model-enabled Privacy Protection and Safety Supervision

该项目为多机器人系统提供了一种协作强化学习方法，可确保安全性并保护隐私。该方法使机器人能够不断学习和适应系统约束内的动态情况。此外，这种新的方法，确保参与机器人的私人信息，如身份和位置，可以在涉及多个参与机器人的协作任务中得到保护。虽然多机器人学习的方法是通用的，但它可以应用于由自主和半自主汽车和卡车驱动的道路上的智能交通系统。 该项目将基于模型的安全性与无模型强化学习相结合，使强化学习适用于安全关键的协作多机器人系统。它将首先解决单机器人强化学习问题，使用基于Koopman的深度安全规则来处理一般非线性机器人系统，以保证安全性，同时保持学习效率。其结果将扩展到多机器人集体强化学习，其中机器人被部署在共享，竞争或资源受限的环境中。通过利用协作学习的固有动态，该项目还将为学习期间收集和共享的数据提供基于动态的隐私保护。不同于传统的隐私机制，要么交易的隐私准确性或招致沉重的计算/通信开销，动态启用的隐私方法可以保持学习最优性，同时招致很少的计算/通信开销。算法和框架将使用数值模拟和真实的轨道上的真实的连接车辆的实验进行评估。该项目的成果将用于丰富研究生和本科生课程。PI还将利用现有的各种正在进行的外展机会，激发K-12学生和社区学院技术人员对STEM的兴趣。该项目得到了跨董事会机器人基础研究计划的支持，由工程局（ENG）和计算机与信息科学与工程局（CISE）共同管理和资助该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project provides a collaborative reinforcement learning approach for multi-robot systems that ensures safety and is privacy-preserving. The approach enables robots to continuously learn and adapt to dynamic situations within the systems constraints. Moreover, this new approach ensures that the private information of the participating robots, such as identity and position, can be protected in collaborative tasks involving multiple participating robots. While the approach for multi-robot learning is general, it has application to intelligent transportation systems on roadways driven by autonomous and semi-autonomous cars and trucks. A demonstration of this approach is to be conducted in a full-scale test environment of a realistic urban setting.This project combines model-based safety with model-free reinforcement learning to enable reinforcement learning's applicability to safety-critical collaborative multi-robot systems. It will first address single-robot reinforcement learning using a deep Koopman-based safety regulation for general nonlinear robotic systems to guarantee safety while retaining learning efficiency. The result will then be extended to multi-robot collective reinforcement learning where robots are deployed in shared, contested, or resource-constrained environments. By exploiting the inherent dynamics of collaborative learning, the project will also enable dynamics-based privacy protection for collected and shared data during learning. Different from conventional privacy mechanisms that either trade accuracy for privacy or incur heavy computation/communication overhead, the dynamics-enabled privacy approach can maintain learning optimality while incurring little computation/communication overhead. The algorithms and frameworks will be evaluated using both numerical simulations and experiments with real connected vehicles on real tracks. Results of the project will be used to enrich both graduate and undergraduate courses. The PIs will also use existing various on-going outreach opportunities to energize interests in STEM in K-12 students and community college technicians.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.