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

CRII：RI：自主代理可行性的可解释框架和变革性应用

• 批准号: 2246221
• 负责人: Stas Tiomkin
• 金额: $ 17.49万
• 依托单位: San Jose State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246221&HistoricalAwards=false
• 关键词: CRII; RI; Interpretable; Framework; Transformative

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.

该项目通过研究机器人的“生存能力”来推进机器人智能的知识。生存能力是指系统在发生不利的关键事件后自主维护或恢复功能的能力，是鲁棒智能和自主性的一个示例。严重事件是机器人倒立并且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恢复状态的无人水下航行器、机械臂陷入杂乱中或由于制动性能不佳而导致的汽车。机械故障。该项目的目标是开发一个设计可行（可自我恢复）的人工智能体的框架，主要挑战是制定一个可以针对各种场景计算的可行性指标。该项目的第一个任务是根据控制系统的基本属性开发一种有效的智能体生存能力度量，以及用于其计算的有效计算方法，第二个任务是证明该框架对于有用问题的适用性。 （我）步行机器人的自我恢复和（ii）汽车制动器的自我维护该项目不仅增进了对机器人可行性的理解，而且还提出了用于控制和分析动力系统的新机器学习方法。为了实现其目标，该项目。该项目将把李亚普诺夫指数的经典概念扩展到“智能体诱发的李亚普诺夫指数”，AILE，它优先考虑具有不同潜力的状态，并且具有大空间的有效可控状态，AILE允许人工智能体。自主训练，维护自身或恢复能力，而不需要针对特定​​问题的外部提供的奖励函数，将开发用于计算已知和未知动态的 AILE 指标，并允许部分和完全可观察的状态。该框架的广泛适用性将在两个变革性应用中得到证明：运动代理的自我恢复和汽车制动器中混沌“粘滑”摩擦的控制该项目并不是按照传统学科的方式构建的。该项目将为完全自主的智能体打开新的研究方向和技术之门，这些智能体具有在危险情况下替代和/或协助人类的巨大潜力。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。