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Perceiving Systems Book Chapter ElephantBook: Participatory Human–AI Elephant Population Monitoring Kulits, P., Wall, J., Beery, S. In Collaborative Intelligence: How Humans and AI Are Transforming Our World, 173-196, 7, (Editors: Lane, Mira and Sethumadhavan, Arathi), The MIT Press, Cambridge, Massachusetts, December 2024 (Published) URL BibTeX
Empirical Inference Book Chapter Natural Language Processing for Policymaking Jin, Z., Mihalcea, R. In Handbook of Computational Social Science for Policy, 141-162, 7, (Editors: Bertoni, E. and Fontana, M. and Gabrielli, L. and Signorelli, S. and Vespe, M.), Springer International Publishing, 2023 (Published) DOI BibTeX
Rationality Enhancement Book Chapter Life Improvement Science Lieder, F., Prentice, M. In Encyclopedia of Quality of Life and Well-Being Research, Springer, November 2022 (Published) DOI BibTeX
Micro, Nano, and Molecular Systems Book Chapter Magnetic Micro-/Nanopropellers for Biomedicine Qiu, T., Jeong, M., Goyal, R., Kadiri, V., Sachs, J., Fischer, P. In Field-Driven Micro and Nanorobots for Biology and Medicine, 389-410, 16, (Editors: Sun, Y. and Wang, X. and Yu, J.), Springer, Cham, November 2022 (Published)
In nature, many bacteria swim by rotating their helical flagella. A particularly promising class of artificial micro- and nano-robots mimic this propeller-like propulsion mechanism to move through fluids and tissues for applications in minimally-invasive medicine. Several fundamental challenges have to be overcome in order to build micro-machines that move similar to bacteria for in vivo applications. Here, we review recent advances of magnetically-powered micro-/nano-propellers. Four important aspects of the propellers – the geometrical shape, the fabrication method, the generation of magnetic fields for actuation, and the choice of biocompatible magnetic materials – are highlighted. First, the fundamental requirements are elucidated that arise due to hydrodynamics at low Reynolds (Re) number. We discuss the role that the propellers’ shape and symmetry play in realizing effective propulsion at low Re. Second, the additive nano-fabrication method Glancing Angle Deposition is discussed as a versatile technique to quickly grow large numbers of designer nano-helices. Third, systems to generate rotating magnetic fields via permanent magnets or electromagnetic coils are presented. And finally, the biocompatibility of the magnetic materials is discussed. Iron-platinum is highlighted due to its biocompatibility and its superior magnetic properties, which is promising for targeted delivery, minimally-invasive magnetic nano-devices and biomedical applications.
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Organizational Leadership and Diversity Book Chapter Stigmatization of Women in the Workplace: Sources of Stigma and its Consequences at the Individual, Organizational and Societal Level Keplinger, K., Smith, A. In Diversity in Action, 23-28, Emerald Publishing Limited, Howard House, Wagon Lane, Bingley BD16 1WA, UK, September 2022 (Published)
Gender balance has been a declared goal in business and society for decades as gender diversity leads to more equality and better decision-making, enhances financial performance of organizations, and fosters creativity and innovation. Although there is a steady upward trend in the number of women actively participating in the workplace, there is still a dearth of women in top leadership positions. This motivates a closer look at the reasons why this happens. Stigmatization – a social process of disapproval based on stereotypes or particular distinguishing characteristics of individuals (e.g. gender) – has been recognized as one of the primary explanations for the barriers to career advancement of women. This chapter aims to address workplace inequality by analysing different sources of stigma women face in the workplace. Previous research has mostly focused on visible sources of stigma, such as gender or race/ethnicity. We propose to go beyond visible sources of stigma and expand the focus to other physical (e.g. physical appearance, age, childbearing age), emotional (e.g. mental health) and societal (e.g. flexibility) sources of stigma. We are especially interested in the consequences of stigma for women in the workplace. Stigmatization of women is a multi-level process, so this chapter focuses on the antecedents (sources of stigma) and outcomes (consequences of stigma) for women at the individual level, organizational level and the societal level. The proposed chapter will make contributions to the areas of management, diversity and gender studies.
Stigmatization of Women DOI URL BibTeX
Empirical Inference Book Chapter CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations Salewski, L., Koepke, A. S., Lensch, H. P. A., Akata, Z. In xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, 69-88, (Editors: Holzinger, Andreas and Goebel, Randy and Fong, Ruth and Moon, Taesup and Müller, Klaus-Robert and Samek, Wojciech), Springer International Publishing, 2022 (Published) DOI BibTeX
Empirical Inference Book Chapter Causal Models for Dynamical Systems Peters, J., Bauer, S., Pfister, N. In Probabilistic and Causal Inference: The Works of Judea Pearl, 671-690, 1, Association for Computing Machinery, 2022 (Published) arXiv DOI BibTeX
Empirical Inference Book Chapter Causality for Machine Learning Schölkopf, B. In Probabilistic and Causal Inference: The Works of Judea Pearl, 765-804, 1, Association for Computing Machinery, New York, NY, USA, 2022 (Published) arXiv DOI BibTeX
Empirical Inference Probabilistic Learning Group Book Chapter Towards Causal Algorithmic Recourse Karimi, A. H., von Kügelgen, J., Schölkopf, B., Valera, I. In xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, 139-166, (Editors: Holzinger, Andreas and Goebel, Randy and Fong, Ruth and Moon, Taesup and Müller, Klaus-Robert and Samek, Wojciech), Springer International Publishing, 2022 (Published) DOI BibTeX
Robotic Materials Book Chapter Electriflow: Augmenting Books With Tangible Animation Using Soft Electrohydraulic Actuators Purnendu, , Novack, S., Acome, E., Alistar, M., Keplinger, C., Gross, M. D., Bruns, C., Leithinger, D. In ACM SIGGRAPH 2021 Labs, 1-2, Association for Computing Machinery, SIGGRAPH, August 2021 (Published)
We present Electriflow: a method of augmenting books with tangible animation employing soft electrohydraulic actuators. These actuators are compact, silent and fast in operation, and can be fabricated with commodity materials. They generate an immediate hydraulic force upon electrostatic activation without an external fluid supply source, enabling a simple and self-contained design. Electriflow actuators produce an immediate shape transition from flat to folded state which enabled their seamless integration into books. For the Emerging Technologies exhibit, we will demonstrate the prototype of a book augmented with the capability of tangible animation.
Supplemental Material DOI URL BibTeX
Embodied Vision Book Chapter TUM Flyers: Vision-Based MAV Navigation for Systematic Inspection of Structures Usenko, V., Stumberg, L. V., Stückler, J., Cremers, D. In Bringing Innovative Robotic Technologies from Research Labs to Industrial End-users: The Experience of the European Robotics Challenges, 136:189-209, Springer Tracts in Advanced Robotics, Springer International Publishing, 2020 (Published) DOI URL BibTeX
Micro, Nano, and Molecular Systems Book Chapter Nanoscale robotic agents in biological fluids and tissues Palagi, S., Walker, D. Q. T., Fischer, P. In The Encyclopedia of Medical Robotics, 2:19-42, 2, (Editors: Desai, J. P. and Ferreira, A.), World Scientific, October 2018 (Published)
Nanorobots are untethered structures of sub-micron size that can be controlled in a non-trivial way. Such nanoscale robotic agents are envisioned to revolutionize medicine by enabling minimally invasive diagnostic and therapeutic procedures. To be useful, nanorobots must be operated in complex biological fluids and tissues, which are often difficult to penetrate. In this chapter, we first discuss potential medical applications of motile nanorobots. We briefly present the challenges related to swimming at such small scales and we survey the rheological properties of some biological fluids and tissues. We then review recent experimental results in the development of nanorobots and in particular their design, fabrication, actuation, and propulsion in complex biological fluids and tissues. Recent work shows that their nanoscale dimension is a clear asset for operation in biological tissues, since many biological tissues consist of networks of macromolecules that prevent the passage of larger micron-scale structures, but contain dynamic pores through which nanorobots can move.
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Empirical Inference Book Chapter Methods in Psychophysics Wichmann, F. A., Jäkel, F. In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (Published) BibTeX
Empirical Inference Book Chapter Policy Gradient Methods Peters, J., Bagnell, J. In Encyclopedia of Machine Learning and Data Mining, 982-985, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (Published) URL BibTeX
Empirical Inference Autonomous Motion Book Chapter Robot Learning Peters, J., Lee, D., Kober, J., Nguyen-Tuong, D., Bagnell, J., Schaal, S. In Springer Handbook of Robotics, 357-394, 15, 2nd, (Editors: Siciliano, Bruno and Khatib, Oussama), Springer International Publishing, 2017 (Published) BibTeX
Empirical Inference Book Chapter Robot Learning Peters, J., Tedrake, R., Roy, N., Morimoto, J. In Encyclopedia of Machine Learning and Data Mining, 1106-1109, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (Published) DOI BibTeX
Empirical Inference Book Chapter Statistical Asymmetries Between Cause and Effect Janzing, D. In Time in Physics, 129-139, Tutorials, Schools, and Workshops in the Mathematical Sciences, (Editors: Renner, Renato and Stupar, Sandra), Springer International Publishing, Cham, 2017 (Published) DOI URL BibTeX
Empirical Inference Book Chapter Unsupervised clustering of EOG as a viable substitute for optical eye-tracking Flad, N., Fomina, T., Bülthoff, H. H., Chuang, L. L. In First Workshop on Eye Tracking and Visualization (ETVIS 2015), 151-167, Mathematics and Visualization, (Editors: Burch, M., Chuang, L., Fisher, B., Schmidt, A., and Weiskopf, D.), Springer, 2017 (Published) DOI BibTeX
Autonomous Motion Book Chapter Locally Weighted Regression for Control Ting, J., Meier, F., Vijayakumar, S., Schaal, S. In Encyclopedia of Machine Learning and Data Mining, 1-14, Springer US, Boston, MA, 2016 DOI URL BibTeX
Empirical Inference Book Chapter Nonlinear functional causal models for distinguishing cause from effect Zhang, K., Hyvärinen, A. In Statistics and Causality: Methods for Applied Empirical Research, 185-201, 8, 1st, (Editors: Wolfgang Wiedermann and Alexander von Eye), John Wiley & Sons, Inc., 2016 (Published) BibTeX
Physical Intelligence Book Chapter Untethered Magnetic Micromanipulation Diller, E., Sitti, M. In Micro-and Nanomanipulation Tools, 13, 10, Wiley-VCH Verlag GmbH & Co. KGaA, November 2015
This chapter discusses the methods and state of the art in microscale manipulation in remote environments using untethered microrobotic devices. It focuses on manipulation at the size scale of tens to hundreds of microns, where small size leads to a dominance of microscale physical effects and challenges in fabrication and actuation. To motivate the challenges of operating at this size scale, the chapter includes coverage of the physical forces relevant to microrobot motion and manipulation below the millimeter-size scale. It then introduces the actuation methods commonly used in untethered manipulation schemes, with particular focus on magnetic actuation due to its wide use in the field. The chapter divides these manipulation techniques into two types: contact manipulation, which relies on direct pushing or grasping of objects for motion, and noncontact manipulation, which relies indirectly on induced fluid flow from the microrobot motion to move objects without any direct contact.
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Empirical Inference Book Chapter Kernel methods in medical imaging Charpiat, G., Hofmann, M., Schölkopf, B. In Handbook of Biomedical Imaging, 63-81, 4, (Editors: Paragios, N., Duncan, J. and Ayache, N.), Springer, Berlin, Germany, June 2015 (Published) Web URL BibTeX
Autonomous Motion Intelligent Control Systems Book Chapter Lernende Roboter Trimpe, S. In Jahrbuch der Max-Planck-Gesellschaft, Max Planck Society, May 2015, (popular science article in German) URL BibTeX
Empirical Inference Book Chapter Justifying Information-Geometric Causal Inference Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B. In Measures of Complexity: Festschrift for Alexey Chervonenkis, 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (Published) DOI BibTeX
Empirical Inference Book Chapter Fuzzy Fibers: Uncertainty in dMRI Tractography Schultz, T., Vilanova, A., Brecheisen, R., Kindlmann, G. In Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, 79-92, 8, Mathematics + Visualization, (Editors: Hansen, C. D., Chen, M., Johnson, C. R., Kaufman, A. E. and Hagen, H.), Springer, 2014 (Published) BibTeX
Empirical Inference Book Chapter Higher-Order Tensors in Diffusion Imaging Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L. In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, 129-161, Mathematics + Visualization, (Editors: Westin, C.-F., Vilanova, A. and Burgeth, B.), Springer, 2014 (Published) BibTeX
Empirical Inference Book Chapter Nonconvex Proximal Splitting with Computational Errors Sra, S. In Regularization, Optimization, Kernels, and Support Vector Machines, 83-102, 4, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), CRC Press, 2014 (Published) BibTeX
Perceiving Systems Book Chapter Simulated Annealing Gall, J. In Encyclopedia of Computer Vision, 737-741, 0, (Editors: Ikeuchi, K. ), Springer Verlag, 2014, to appear BibTeX
Empirical Inference Book Chapter Single-Source Domain Adaptation with Target and Conditional Shift Zhang, K., Schölkopf, B., Muandet, K., Wang, Z., Zhou, Z., Persello, C. In Regularization, Optimization, Kernels, and Support Vector Machines, 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 BibTeX
Empirical Inference Book Chapter A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs) Grosse-Wentrup, M., Schölkopf, B. In Brain-Computer Interface Research, 39-51, 4, SpringerBriefs in Electrical and Computer Engineering, (Editors: Guger, C., Allison, B. Z. and Edlinger, G.), Springer, 2013 PDF DOI BibTeX
Empirical Inference Book Chapter On the Relations and Differences between Popper Dimension, Exclusion Dimension and VC-Dimension Seldin, Y., Schölkopf, B. In Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 53-57, 6, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (Published) BibTeX
Empirical Inference Book Chapter Semi-supervised learning in causal and anticausal settings Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., Mooij, J. In Empirical Inference, 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 DOI BibTeX
Empirical Inference Book Chapter Tractable large-scale optimization in machine learning Sra, S. In Tractability: Practical Approaches to Hard Problems, 202-230, 7, (Editors: Bordeaux, L., Hamadi , Y., Kohli, P. and Mateescu, R. ), Cambridge University Press , 2013 (Published) BibTeX
Empirical Inference Book Chapter Expectation-Maximization methods for solving (PO)MDPs and optimal control problems Toussaint, M., Storkey, A., Harmeling, S. In Inference and Learning in Dynamic Models, (Editors: Barber, D., Cemgil, A.T. and Chiappa, S.), Cambridge University Press, Cambridge, UK, January 2012 (In press) PDF BibTeX
Empirical Inference Book Chapter Higher-Order Tensors in Diffusion MRI Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L. In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, (Editors: Westin, C. F., Vilanova, A. and Burgeth, B.), Springer, 2012 (Accepted) BibTeX
Empirical Inference Book Chapter Inferential structure determination from NMR data Habeck, M. In Bayesian methods in structural bioinformatics, 287-312, (Editors: Hamelryck, T., Mardia, K. V. and Ferkinghoff-Borg, J.), Springer, New York, 2012 BibTeX
Empirical Inference Book Chapter Reinforcement Learning in Robotics: A Survey Kober, J., Peters, J. In Reinforcement Learning, 12:579-610, (Editors: Wiering, M. and Otterlo, M.), Springer, Berlin, Germany, 2012
As most action generation problems of autonomous robots can be phrased in terms of sequential decision problems, robotics offers a tremendously important and interesting application platform for reinforcement learning. Similarly, the real-world challenges of this domain pose a major real-world check for reinforcement learning. Hence, the interplay between both disciplines can be seen as promising as the one between physics and mathematics. Nevertheless, only a fraction of the scientists working on reinforcement learning are sufficiently tied to robotics to oversee most problems encountered in this context. Thus, we will bring the most important challenges faced by robot reinforcement learning to their attention. To achieve this goal, we will attempt to survey most work that has successfully applied reinforcement learning to behavior generation for real robots. We discuss how the presented successful approaches have been made tractable despite the complexity of the domain and will study how representations or the inclusion of prior knowledge can make a significant difference. As a result, a particular focus of our chapter lies on the choice between model-based and model-free as well as between value function-based and policy search methods. As a result, we obtain a fairly complete survey of robot reinforcement learning which should allow a general reinforcement learning researcher to understand this domain.
Web DOI BibTeX
Empirical Inference Book Chapter Robot Learning Sigaud, O., Peters, J. In Encyclopedia of the sciences of learning, (Editors: Seel, N.M.), Springer, Berlin, Germany, 2012 Web BibTeX
Empirical Inference Book Chapter Projected Newton-type methods in machine learning Schmidt, M., Kim, D., Sra, S. In Optimization for Machine Learning, 305-330, (Editors: Sra, S., Nowozin, S. and Wright, S. J.), MIT Press, Cambridge, MA, USA, December 2011
We consider projected Newton-type methods for solving large-scale optimization problems arising in machine learning and related fields. We first introduce an algorithmic framework for projected Newton-type methods by reviewing a canonical projected (quasi-)Newton method. This method, while conceptually pleasing, has a high computation cost per iteration. Thus, we discuss two variants that are more scalable, namely, two-metric projection and inexact projection methods. Finally, we show how to apply the Newton-type framework to handle non-smooth objectives. Examples are provided throughout the chapter to illustrate machine learning applications of our framework.
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Empirical Inference Book Chapter Statistical Learning Theory: Models, Concepts, and Results von Luxburg, U., Schölkopf, B. In Handbook of the History of Logic, Vol. 10: Inductive Logic, 10:651-706, (Editors: Gabbay, D. M., Hartmann, S. and Woods, J. H.), Elsevier North Holland, Amsterdam, Netherlands, May 2011
Statistical learning theory provides the theoretical basis for many of today's machine learning algorithms and is arguably one of the most beautifully developed branches of artificial intelligence in general. It originated in Russia in the 1960s and gained wide popularity in the 1990s following the development of the so-called Support Vector Machine (SVM), which has become a standard tool for pattern recognition in a variety of domains ranging from computer vision to computational biology. Providing the basis of new learning algorithms, however, was not the only motivation for developing statistical learning theory. It was just as much a philosophical one, attempting to answer the question of what it is that allows us to draw valid conclusions from empirical data. In this article we attempt to give a gentle, non-technical overview over the key ideas and insights of statistical learning theory. We do not assume that the reader has a deep background in mathematics, statistics, or computer science. Given the nature of the subject matter, however, some familiarity with mathematical concepts and notations and some intuitive understanding of basic probability is required. There exist many excellent references to more technical surveys of the mathematics of statistical learning theory: the monographs by one of the founders of statistical learning theory ([Vapnik, 1995], [Vapnik, 1998]), a brief overview over statistical learning theory in Section 5 of [Sch{\"o}lkopf and Smola, 2002], more technical overview papers such as [Bousquet et al., 2003], [Mendelson, 2003], [Boucheron et al., 2005], [Herbrich and Williamson, 2002], and the monograph [Devroye et al., 1996].
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Empirical Inference Book Chapter Robot Learning Peters, J., Tedrake, R., Roy, N., Morimoto, J. In Encyclopedia of Machine Learning, 865-869, Encyclopedia of machine learning, (Editors: Sammut, C. and Webb, G. I.), Springer, New York, NY, USA, January 2011 PDF Web DOI BibTeX
Empirical Inference Book Chapter Cue Combination: Beyond Optimality Rosas, P., Wichmann, F. In Sensory Cue Integration, 144-152, (Editors: Trommershäuser, J., Körding, K. and Landy, M. S.), Oxford University Press, 2011 BibTeX
Empirical Inference Book Chapter Kernel Methods in Bioinformatics Borgwardt, K. In Handbook of Statistical Bioinformatics, 317-334, Springer Handbooks of Computational Statistics ; 3, (Editors: Lu, H.H.-S., Schölkopf, B. and Zhao, H.), Springer, Berlin, Germany, 2011
Kernel methods have now witnessed more than a decade of increasing popularity in the bioinformatics community. In this article, we will compactly review this development, examining the areas in which kernel methods have contributed to computational biology and describing the reasons for their success.
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Empirical Inference Book Chapter What You Expect Is What You Get? Potential Use of Contingent Negative Variation for Passive BCI Systems in Gaze-Based HCI Ihme, K., Zander, T. In Affective Computing and Intelligent Interaction, 6975:447-456, Lecture Notes in Computer Science, (Editors: D’Mello, S., Graesser, A., Schuller, B. and Martin, J.-C.), Springer, Berlin, Germany, 2011
When using eye movements for cursor control in human-computer interaction (HCI), it may be difficult to find an appropriate substitute for the click operation. Most approaches make use of dwell times. However, in this context the so-called Midas-Touch-Problem occurs which means that the system wrongly interprets fixations due to long processing times or spontaneous dwellings of the user as command. Lately it has been shown that brain-computer interface (BCI) input bears good prospects to overcome this problem using imagined hand movements to elicit a selection. The current approach tries to develop this idea further by exploring potential signals for the use in a passive BCI, which would have the advantage that the brain signals used as input are generated automatically without conscious effort of the user. To explore event-related potentials (ERPs) giving information about the user’s intention to select an object, 32-channel electroencephalography (EEG) was recorded from ten participants interacting with a dwell-time-based system. Comparing ERP signals during the dwell time with those occurring during fixations on a neutral cross hair, a sustained negative slow cortical potential at central electrode sites was revealed. This negativity might be a contingent negative variation (CNV) reflecting the participants’ anticipation of the upcoming selection. Offline classification suggests that the CNV is detectable in single trial (mean accuracy 74.9 %). In future, research on the CNV should be accomplished to ensure its stable occurence in human-computer interaction and render possible its use as a potential substitue for the click operation.
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Empirical Inference Book Chapter Markerless tracking of Dynamic 3D Scans of Faces Walder, C., Breidt, M., Bülthoff, H., Schölkopf, B., Curio, C. In Dynamic Faces: Insights from Experiments and Computation, 255-276, (Editors: Curio, C., Bülthoff, H. H. and Giese, M. A.), MIT Press, Cambridge, MA, USA, December 2010 Web BibTeX
Empirical Inference Book Chapter Policy Gradient Methods Peters, J., Bagnell, J. In Encyclopedia of Machine Learning, 774-776, (Editors: Sammut, C. and Webb, G. I.), Springer, Berlin, Germany, December 2010 PDF Web DOI BibTeX
Empirical Inference Book Chapter From Motor Learning to Interaction Learning in Robots Sigaud, O., Peters, J. In From Motor Learning to Interaction Learning in Robots, 1-12, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010
The number of advanced robot systems has been increasing in recent years yielding a large variety of versatile designs with many degrees of freedom. These robots have the potential of being applicable in uncertain tasks outside wellstructured industrial settings. However, the complexity of both systems and tasks is often beyond the reach of classical robot programming methods. As a result, a more autonomous solution for robot task acquisition is needed where robots adaptively adjust their behaviour to the encountered situations and required tasks. Learning approaches pose one of the most appealing ways to achieve this goal. However, while learning approaches are of high importance for robotics, we cannot simply use off-the-shelf methods from the machine learning community as these usually do not scale into the domains of robotics due to excessive computational cost as well as a lack of scalability. Instead, domain appropriate approaches are needed. In this book, we focus on several core domains of robot learning. For accurate task execution, we need motor learning capabilities. For fast learning of the motor tasks, imitation learning offers the most promising approach. Self improvement requires reinforcement learning approaches that scale into the domain of complex robots. Finally, for efficient interaction of humans with robot systems, we will need a form of interaction learning. This chapter provides a general introduction to these issues and briefly presents the contributions of the subsequent chapters to the corresponding research topics.
Web DOI BibTeX
Empirical Inference Book Chapter Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling Kober, J., Mohler, B., Peters, J. In From Motor Learning to Interaction Learning in Robots, 209-225, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010
Traditional motor primitive approaches deal largely with open-loop policies which can only deal with small perturbations. In this paper, we present a new type of motor primitive policies which serve as closed-loop policies together with an appropriate learning algorithm. Our new motor primitives are an augmented version version of the dynamical system-based motor primitives [Ijspeert et al(2002)Ijspeert, Nakanishi, and Schaal] that incorporates perceptual coupling to external variables. We show that these motor primitives can perform complex tasks such as Ball-in-a-Cup or Kendama task even with large variances in the initial conditions where a skilled human player would be challenged. We initialize the open-loop policies by imitation learning and the perceptual coupling with a handcrafted solution. We first improve the open-loop policies and subsequently the perceptual coupling using a novel reinforcement learning method which is particularly well-suited for dynamical system-based motor primitives.
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Empirical Inference Book Chapter Learning Continuous Grasp Affordances by Sensorimotor Exploration Detry, R., Baseski, E., Popovic, M., Touati, Y., Krüger, N., Kroemer, O., Peters, J., Piater, J. In From Motor Learning to Interaction Learning in Robots, 451-465, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010
We develop means of learning and representing object grasp affordances probabilistically. By grasp affordance, we refer to an entity that is able to assess whether a given relative object-gripper configuration will yield a stable grasp. These affordances are represented with grasp densities, continuous probability density functions defined on the space of 3D positions and orientations. Grasp densities are registered with a visual model of the object they characterize. They are exploited by aligning them to a target object using visual pose estimation. Grasp densities are refined through experience: A robot “plays” with an object by executing grasps drawn randomly for the object’s grasp density. The robot then uses the outcomes of these grasps to build a richer density through an importance sampling mechanism. Initial grasp densities, called hypothesis densities, are bootstrapped from grasps collected using a motion capture system, or from grasps generated from the visual model of the object. Refined densities, called empirical densities, represent affordances that have been confirmed through physical experience. The applicability of our method is demonstrated by producing empirical densities for two object with a real robot and its 3-finger hand. Hypothesis densities are created from visual cues and human demonstration.
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Empirical Inference Book Chapter Real-Time Local GP Model Learning Nguyen-Tuong, D., Seeger, M., Peters, J. In From Motor Learning to Interaction Learning in Robots, 264:193-207, Studies in Computational Intelligence, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010
For many applications in robotics, accurate dynamics models are essential. However, in some applications, e.g., in model-based tracking control, precise dynamics models cannot be obtained analytically for sufficiently complex robot systems. In such cases, machine learning offers a promising alternative for approximating the robot dynamics using measured data. However, standard regression methods such as Gaussian process regression (GPR) suffer from high computational complexity which prevents their usage for large numbers of samples or online learning to date. In this paper, we propose an approximation to the standard GPR using local Gaussian processes models inspired by [Vijayakumar et al(2005)Vijayakumar, D’Souza, and Schaal, Snelson and Ghahramani(2007)]. Due to reduced computational cost, local Gaussian processes (LGP) can be applied for larger sample-sizes and online learning. Comparisons with other nonparametric regressions, e.g., standard GPR, support vector regression (SVR) and locally weighted proje ction regression (LWPR), show that LGP has high approximation accuracy while being sufficiently fast for real-time online learning.
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