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.

Integrated operation of drinking water treatment plant at Amsterdam water supply

Abstract: Water treatment plants are in general robust and designs are based on the performance of individual processes with pre-set boundary conditions. It is assumed that an integral approach of the entire treatment plant can lead to more efficient operation. Taking into account the developments in sensoring, automation and computation, it is a challenge to improve quality and reliability of the treatment plants and to make maximal use of the installed infrastructure, postponing new investments. At Amsterdam Water Supply (AWS), the first steps have been taken to come to an integral dynamic model of the total water treatment plant and the use of this model as an instrument for integral control. The parameters influencing the performance of the water treatment process will be incorporated in an overall model evaluating the goal factors quality (good, constant and reliable), quantity, costs, environmental impact (low residuals level), redundancy and flexibility. For several individual processes at AWS models have already been developed during the last few years, like models for the ozone process, biological activated carbon filtration and pellet softening. For the final calibration and validation pilot reactors are automated and on-line data are collected. Criteria for evaluation are developed to realise an optimal control of the individual processes in interaction with the goal factors of the total treatment process.

Optimistic planning for sparsely stochastic systems

Abstract: We propose an online planning algorithm for finite-action, sparsely stochastic Markov decision processes, in which the random state transitions can only end up in a small number of possible next states. The algorithm builds a planning tree by iteratively expanding states, where each expansion exploits sparsity to add all possible successor states. Each state to expand is actively chosen to improve the knowledge about action quality, and this allows the algorithm to return a good action after a strictly limited number of expansions. More specifically, the active selection method is optimistic in that it chooses the most promising states first, so the novel algorithm is called optimistic planning for sparsely stochastic systems. We note that the new algorithm can also be seen as model-predictive (receding-horizon) control. The algorithm obtains promising numerical results, including the successful online control of a simulated HIV infection with stochastic drug effectiveness.

Directional Singularity-Robust Torque Control for Gyroscopic Actuators

Abstract: Gyroscopic actuation is appealing for wearable applications due to its ability to impart free moments on a body without exoskeletal structures on the joints. We recently proposed an unobtrusive balancing aid consisting of multiple parallel-mounted control moment gyroscopes (CMGs) contained within a backpack-like orthopedic corset. Using conventional CMG control techniques, geometric singularities result in a number of performance issues, including either unintended oscillations or freezing of the gimbals at certain alignments, which are typically mitigated by the addition of redundant actuators or by allowing errors in the generated moment; however, because of the minimalistic design of the proposed device and focus on accurate moment tracking, a new methodology is required. In this paper, a control scheme is proposed for nonredundant CMG systems in which oscillations at saturated states are avoided and all remaining singularities are efficiently escaped by exploiting the system geometry; due to its use of classification-specific singularity proximity measures that account for the command moment orientation, it is named the directional singularity-robust control law. The performance of this control law is assessed in both simulations and hardware testing. The proposed method is suitable for a wide range of CMG systems, including both balancing and aerospace applications.

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.