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

本项目通过对机器人“生存能力”的研究，促进了对机器人智能的认识。生存能力是指系统在发生不利的关键事件后自主维护自身或恢复功能的能力，是健壮智能和自主性的理想特征。关键事件的一个例子是机器人倒立着陆，在没有外部帮助的情况下无法站立。其他例子包括处于无法回收状态的无人水下机器人，陷入混乱的机械臂，或者由于机械故障而刹车性能不佳的汽车。该项目的主要目标是为设计可行的(可自愈的)人工制剂开发一个框架。主要的挑战是制定一种可以在各种情况下计算的生存能力指标。这一指标需要根据电机和传感器读数进行计算。这个项目的第一个任务是从控制系统的基本属性中开发一个有效的代理生存能力度量，以及计算它的有效方法。第二个任务是证明该框架对于(I)行走机器人的自我恢复和(Ii)汽车制动器的自我维护的有用问题的适用性。该项目不仅促进了对机器人生存能力的理解，而且为动力学系统的控制和分析提供了新的机器学习方法。为了实现其目标，该项目将经典的Lyapunov指数的概念扩展为代理诱导的Lyapunov指数，AILE对具有不同潜力的状态进行优先排序，并且具有大量有效可控的状态空间。AILE允许人工代理自主训练、维护自己或恢复能力，而不需要特定于问题的外部提供的奖励功能。将开发计算方法，用于计算具有已知和未知动力学以及部分和完全可观察状态的AILE度规。这一能力将使该框架具有广泛的适用性。它将在两个变革性的应用中得到展示：运动剂的自我恢复，以及控制汽车刹车中的混沌“粘滑”摩擦。这个项目不是沿着传统学科的界限来框定的，而是在机器学习、动力系统、力学和信息论之间架起的桥梁。该项目将为全自主代理打开新的研究方向和技术的大门，这些代理具有在危险情况下取代和/或帮助人类的巨大潜力，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。

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.