Abstract

A robot is proficient if its performance for its task(s) satisfies a specific standard. While the design of autonomous robots often emphasizes such proficiency, another important attribute of autonomous robot systems is their ability to evaluate their own proficiency. A robot should be able to conduct proficiency self-assessment (PSA), i.e. assess how well it can perform a task before, during, and after it has attempted the task. We propose the assumption-alignment tracking (AAT) method, which provides time-indexed assessments of the veracity of robot generators' assumptions, for designing autonomous robots that can effectively evaluate their own performance. AAT can be considered as a general framework for using robot sensory data to extract useful features, which are then used to build data-driven PSA models. We develop various AAT-based data-driven approaches to PSA from different perspectives. First, we use AAT for estimating robot performance. AAT features encode how the robot's current running condition varies from the normal condition, which correlates with the deviation level between the robot's current performance and normal performance. We use the k-nearest neighbor algorithm to model that correlation. Second, AAT features are used for anomaly detection. We treat anomaly detection as a one-class classification problem where only data from the robot operating in normal conditions are used in training, decreasing the burden on acquiring data in various abnormal conditions. The cluster boundary of data points from normal conditions, which serves as the decision boundary between normal and abnormal conditions, can be identified by mainstream one-class classification algorithms. Third, we improve PSA models that predict robot success/failure by introducing meta-PSA models that assess the correctness of PSA models. The probability that a PSA model's prediction is correct is conditioned on four features: 1) the mean distance from a test sample to its nearest neighbors in the training set; 2) the predicted probability of success made by the PSA model; 3) the ratio between the robot's current performance and its performance standard; and 4) the percentage of the task the robot has already completed. Meta-PSA models trained on the four features using a Random Forest algorithm improve PSA models with respect to both discriminability and calibration. Finally, we explore how AAT can be used to generate a new type of explanation of robot behavior/policy from the perspective of a robot's proficiency. AAT provides three pieces of information for explanation generation: (1) veracity assessment of the assumptions on which the robot's generators rely; (2) proficiency assessment measured by the probability that the robot will successfully accomplish its task; and (3) counterfactual proficiency assessment computed with the veracity of some assumptions varied hypothetically. The information provided by AAT fits the situation awareness-based framework for explainable artificial intelligence. The efficacy of AAT is comprehensively evaluated using robot systems with a variety of robot types, generators, hardware, and tasks, including a simulated robot navigating in a maze-based (discrete time) Markov chain environment, a simulated robot navigating in a continuous environment, and both a simulated and a real-world robot arranging blocks of different shapes and colors in a specific order on a table.

Degree

PhD

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

https://lib.byu.edu/about/copyright/