Robert Babuska

Bio: Robert Babuska is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Fuzzy logic & Reinforcement learning. The author has an hindex of 56, co-authored 371 publications receiving 15388 citations. Previous affiliations of Robert Babuska include Carnegie Mellon University & Czech Technical University in Prague.

Fuzzy modeling - a control engineering perspective

Abstract: Recent advances in the theory of fuzzy modeling and a number of successful real-world applications show that fuzzy models can be efficiently applied to complex nonlinear systems untractable with standard linear methods. Besides the capability of modeling nonlinear systems, there are other properties that make fuzzy models interesting not only theoretically but also for the industrial practice. This paper attempts to overview various approaches to fuzzy modeling, seen from the control engineering perspective. Special attention is focused on the construction of fuzzy models from numerical data and the possibility of incorporating a priori knowledge about the system and some open problems are highlighted. >

Model-plant mismatch compensation using reinforcement learning

Abstract: Learning-based approaches are suitable for the control of systems with unknown dynamics. However, learning from scratch involves many trials with exploratory actions until a good control policy is discovered. Real robots usually cannot withstand the exploratory actions and suffer damage. This problem can be circumvented by combining learning with a model-based control. In this letter, we employ a nominal model-predictive controller that is impeded by the presence of an unknown model-plant mismatch. To compensate for the mismatch, we propose two approaches of combining reinforcement learning with the nominal controller. The first approach learns a compensatory control action that minimizes the same performance measure as is minimized by the nominal controller. The second approach learns a compensatory signal from a difference of a transition predicted by the internal model and an actual transition. We compare the approaches on a robot attached to the ground and performing a setpoint reaching task in simulations. We implement the better approach on the real robot and demonstrate successful learning results.

Transparent Fuzzy Modelling

Abstract: One of the objectives of machine learning is to enable intelligent systems to acquire knowledge in a highly automated manner. In systems modelling and control engineering, fuzzy systems have shown to be highly suitable for the modelling of complex and uncertain systems. Recently, the interest in fuzzy systems has shifted from the seminal ideas about modelling the process or the behaviour of operators by knowledge acquisition towards a data-driven approach. Reasons to choose fuzzy systems instead of modelling techniques such as neural networks, radial basis functions, genetic algorithms or splines, are mainly the possibility of integrating logical information processing with the attractive mathematical properties of general function approximators. Furthermore, the rule-based structure of fuzzy systems makes analysis easier. The fuzzy sets in the rules represent linguistic qualitative terms that approximate the human-like way of information quantization. However, many of the data-driven fuzzy modelling algorithms that have been developed, aim at good numerical approximation and pay little attention to the semantical properties of the resulting rule base. In this article, we briefly discuss different approaches to data-intensive fuzzy modelling reported in the literature. Next, we present a data-driven approach to fuzzy modelling that provides the user with both accurate and transparent rule bases. The method has two main steps: data exploration by means of fuzzy clustering and fuzzy set aggregation by means of similarity analysis. First, fuzzy relations are identified in the product space of the system's variables and are described by means of fuzzy production rules. Compatible fuzzy concepts defined for the individual variables are then identified and aggregated to produce generalizing concepts, giving a comprehensible rule base with increased semantic properties. The transparent fuzzy modelling approach is demonstrated on a real world problem concerning the modelling of algae growth in lakes.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Abstract: The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

Reinforcement learning in robotics: A survey

Abstract: Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.