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

About This Grant

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.