Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical systems. Computational methods for control systems, particularly those used for developing AI and other machine learning techniques, are also discussed at length. - Provides foundational control theory concepts, along with advanced techniques and the latest advances in adaptive control and robotics - Introduces state-of-the-art learning-based control technologies and their applications in robotics and other complex dynamical systems - Demonstrates computational techniques for control systems - Covers iterative learning impedance control in both human-robot interaction and collaborative robots

Data migration, generally referred to as the process of reading data from their source and inserting them into a target database, is an important element of data extract, transform, and load (ETL) systems. During data migration, errors can occur during data transmission. These errors can directly affect the quality of the data in the target database. Therefore, verifying the correctness of the outcome is a critical component of a data migration operation. Current methods in data migration correctness verification have many limitations, including incompleteness and inaccuracy. This paper describes an innovative method that applies the well-proven checksum methodology to verify the correctness of the data migration outcome. This method performs a thorough and accurate verification on the correctness of the migrated data, mitigating most of the weaknesses associated with current verification methods. This method is also easy to implement and will greatly enhance the quality of data migration operations.

Software updates often involve data migration, especially when converting legacy software implemented to interface with outdated relational database management systems or other nonrelational database electronic files. Moreover, many software applications rely on data migration to import data from a variety of platforms. Usually, database migrations are time consuming and error prone. Based on their experience designing and implementing custom utilities to convert a large number of legacy databases and files in different platforms, RTI computer scientists developed five criteria that need to be considered when evaluating a data migration tool (DMT). These criteria can help users and software development project managers make informed decisions in data conversion tasks, help software developers assess design and implementation considerations for future DMT products, and provide guidelines for database administrators to evaluate a general DMT.

This book introduces state-of-the-art technologies in the field of human-robot interactions. It details advances made in this field in recent decades, including dynamics, controls, design analysis, uncertainties, and modelling. The text will appeal to graduate students, practitioners and researchers in the fields of robotics, computer and cognitive science, and mechanical engineering.

In the first chapter, "Managerial Ability and Open-End Fund Flows", I propose a managerial-ability-based theory to study open-end fund flows. Previous empirical research documents a convex cross-sectional relationship between past performance and mutual fund flows. Using a dynamic rational expectations equilibrium model with endogenous portfolio management, I propose a new economic theory to explain the convex flow-performance relationship. The proposed theory highlights the importance of incorporating active portfolio management into economic analysis of fund flows. A quantitative analysis shows that the fully calibrated model accounts for several empirical regularities of mutual funds.

How many interest rate hikes is quantitative tightening (QT) equivalent to? In this paper, I examine this question based on the preferred-habitat model in Vayanos and Vila (2021). I define the equivalence between rate hikes and QT such that they both have the same impact on the 10-year yield. Based on the model calibrated to fit the nominal Treasury data between 1999 and 2022, I show that a passive roll-off of $2.2 trillion over three years is equivalent to an increase of 29 basis points in the current federal funds rate at normal times. However, during a crisis period with risk aversion being doubled, it is equivalent to a 74 basis point increase. I also quantify the effect of QT implemented by active sales. Lastly, based on the model-based estimates, I show that if the Treasury were to issue bills to offset maturing securities, the resulting equivalent rate hikes in the current federal funds rate would decrease dramatically to 7.4 (12.6) basis points during normal (crisis) periods.

"Uncertainty has qualitatively different implications than risk in studying executive incentives. The authors study the interplay between profitability uncertainty and moral hazard, where profitability is multiplicative with managerial effort. Investors who face greater uncertainty desire faster learning, and consequently offer higher managerial incentives to induce higher effort from the manager. In contrast to the standard negative risk-incentive trade-off this 'learning-by-doing' effect generates a positive relation between probability uncertainty and incentives. They document empirical support for this prediction."--Abstract.