Robot Proficiency Self-Assessment Using Assumption-Alignment Tracking

Abstract

While the design of autonomous robots often emphasizes developing proficient robots, another important attribute of autonomous robot systems is their ability to evaluate their own proficiency. A robot should be able to assess how well it can perform a task before, during, and after it has attempted the task. How can autonomous robots be designed to self-assess their behavior? This article presents the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">assumption-alignment tracking</i> (AAT) method for designing autonomous robots that can effectively evaluate their own performance. In AAT, the robot a) tracks the veracity of assumptions made by the robot's decision-making algorithms to measure how well these algorithms fit, or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">align with</i> , its environment and hardware systems, and b) uses the measurement of alignment to assess the robot's ability to succeed at a given task based on its past experiences. The efficacy of AAT is illustrated through three case studies: a simulated robot navigating in a maze-based (discrete time) Markov chain environment, a simulated robot navigating in a continuous environment, and a real-world robot arranging blocks of different shapes and colors in a specific order on a table. Results show that AAT is able to accurately predict robot performance and, hence, determine robot proficiency in real time.