CRII: RI: Interpretable Framework and Transformative Applications for Viability in Autonomous Agents

该项目通过研究机器人的“生存能力”来推进机器人智能方面的知识。可行性是系统在不利的关键事件后自主维护自身或恢复功能的能力，是鲁棒智能和自主性的期望特征。关键事件的一个示例是机器人倒立着陆，并且在没有外部帮助的情况下无法站起来。其他的例子是无人水下航行器处于无法恢复的状态，机器人手臂陷入混乱，或者由于机械故障而制动性能差的汽车。该项目的主要目标是开发一个可行的（自我恢复）人工代理的设计框架。主要的挑战是制定一个衡量可行性的指标，可以计算各种情况。这个指标需要从电机和传感器读数中计算出来。这个项目的第一个任务是开发一个有效的度量代理的生存能力从控制系统的基本属性，沿着计算效率高的方法计算。第二个任务是证明这个框架的适用性有用的问题（一）步行机器人的自我恢复和（二）汽车制动器的自我维护。本研究计划将传统的李雅普诺夫指数概念延伸至“智能体诱导李雅普诺夫指数”（Agent-Induced Lyapunov Exponents，AILE），以区分具有不同潜能的状态，以及具有大空间的有效可控状态。AILE允许人工智能体自主训练，保持自己或恢复能力，而不需要特定于问题的外部提供的奖励功能。计算方法将被开发用于计算已知和未知动态的AILE度量，以及部分和完全可观察的状态。这一能力将使该框架具有广泛的适用性。它将被证明在两个变革性的应用：自我恢复的运动代理，和控制混乱的“粘滑”摩擦在汽车刹车。这个项目不是沿着传统学科边界的框架，而是机器学习，动力系统，力学和信息理论之间的桥梁。该项目将为全自主智能体的新研究方向和技术打开大门，这些智能体具有巨大的潜力，可以在危险情况下取代和/或协助人类，例如驾驶。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 advances the knowledge in robot intelligence via research on “viability” of robots. Viability is the ability of a system to autonomously maintain itself or recover functionality after an adverse critical event and is a desired feature of robust intelligence and autonomy. An example of a critical event is a robot landing upside down and being unable to stand up without an external assistance. Other examples are an unmanned underwater vehicle in an unrecoverable state, a robotic arm getting stuck in clutter, or a car with poor braking performance due to a mechanical failure. The principal objective of this project is to develop a framework for the design of viable (self-recoverable) artificial agents. The main challenge is to formulate a metric for viability that can be calculated for various scenarios. This metric needs to be computable from the motor and sensor readings. The first task of this project is to develop an effective metric for agent viability from the essential properties of control systems, along with computationally efficient methods for its calculation. The second task is to demonstrate the applicability of this framework for useful problems in (i) self-recovery of walking robots and (ii) self-maintenance of car brakes. This project not only advances the understanding of robot viability, but also proposes new machine-learning approaches for control and analysis of dynamical systems.To achieve its goals, this project will extend the classical notion of Lyapunov exponents towards ’Agent-Induced Lyapunov Exponents’, AILE, which prioritize states with diverse potentialities, and with a large space of effectively controllable states. AILE allows artificial agents to train autonomously, maintaining themselves or recovering capabilities without the need for problem-specific externally provided reward functions. Computational methods will be developed for the calculation of the AILE metric with known and unknown dynamics, and with partially and fully observable state. This capacity will allow for the broad applicability of the framework. It will be demonstrated in two transformative applications: self-recovery of locomotion agents, and control of chaotic ’stick-slip’ friction in car brakes. This project is not framed along the lines of traditional disciplinary boundaries, but rather bridges between machine learning, dynamical systems, mechanics, and information theory. This project will open doors to new research directions and technology for fully-autonomous agents with an enormous potential for replacing and/or assisting humans in risky situations, such as driving.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.