Header logo is


2019


no image
Convolutional neural networks: A magic bullet for gravitational-wave detection?

Gebhard, T., Kilbertus, N., Harry, I., Schölkopf, B.

Physical Review D, 100(6):063015, American Physical Society, September 2019 (article)

ei

link (url) DOI [BibTex]

2019


link (url) DOI [BibTex]


no image
Data scarcity, robustness and extreme multi-label classification

Babbar, R., Schölkopf, B.

Machine Learning, 108(8):1329-1351, September 2019, Special Issue of the ECML PKDD 2019 Journal Track (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
A 32-channel multi-coil setup optimized for human brain shimming at 9.4T

Aghaeifar, A., Zhou, J., Heule, R., Tabibian, B., Schölkopf, B., Jia, F., Zaitsev, M., Scheffler, K.

Magnetic Resonance in Medicine, 2019, (Early View) (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
Enhancing Human Learning via Spaced Repetition Optimization

Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the National Academy of Sciences, 2019, PNAS published ahead of print January 22, 2019 (article)

ei

DOI Project Page Project Page [BibTex]

DOI Project Page Project Page [BibTex]


Thumb xl screenshot 2019 03 25 at 14.29.22
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots

Büchler, D., Calandra, R., Peters, J.

2019 (article) Submitted

Abstract
High-speed and high-acceleration movements are inherently hard to control. Applying learning to the control of such motions on anthropomorphic robot arms can improve the accuracy of the control but might damage the system. The inherent exploration of learning approaches can lead to instabilities and the robot reaching joint limits at high speeds. Having hardware that enables safe exploration of high-speed and high-acceleration movements is therefore desirable. To address this issue, we propose to use robots actuated by Pneumatic Artificial Muscles (PAMs). In this paper, we present a four degrees of freedom (DoFs) robot arm that reaches high joint angle accelerations of up to 28000 °/s^2 while avoiding dangerous joint limits thanks to the antagonistic actuation and limits on the air pressure ranges. With this robot arm, we are able to tune control parameters using Bayesian optimization directly on the hardware without additional safety considerations. The achieved tracking performance on a fast trajectory exceeds previous results on comparable PAM-driven robots. We also show that our system can be controlled well on slow trajectories with PID controllers due to careful construction considerations such as minimal bending of cables, lightweight kinematics and minimal contact between PAMs and PAMs with the links. Finally, we propose a novel technique to control the the co-contraction of antagonistic muscle pairs. Experimental results illustrate that choosing the optimal co-contraction level is vital to reach better tracking performance. Through the use of PAM-driven robots and learning, we do a small step towards the future development of robots capable of more human-like motions.

ei

Arxiv Video [BibTex]


no image
Inferring causation from time series with perspectives in Earth system sciences

Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M., van Nes, E., Peters, J., Quax, R., Reichstein, M., Scheffer, M. S. B., Spirtes, P., Sugihara, G., Sun, J., Zhang, K., Zscheischler, J.

Nature Communications, 2019 (article) In revision

ei

[BibTex]

[BibTex]

2017


no image
Generalized exploration in policy search

van Hoof, H., Tanneberg, D., Peters, J.

Machine Learning, 106(9-10):1705-1724 , (Editors: Kurt Driessens, Dragi Kocev, Marko Robnik‐Sikonja, and Myra Spiliopoulou), October 2017, Special Issue of the ECML PKDD 2017 Journal Track (article)

ei

DOI Project Page [BibTex]

2017


DOI Project Page [BibTex]


no image
Probabilistic Prioritization of Movement Primitives

Paraschos, A., Lioutikov, R., Peters, J., Neumann, G.

Proceedings of the International Conference on Intelligent Robot Systems, and IEEE Robotics and Automation Letters (RA-L), 2(4):2294-2301, October 2017 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Learning Movement Primitive Libraries through Probabilistic Segmentation

Lioutikov, R., Neumann, G., Maeda, G., Peters, J.

International Journal of Robotics Research, 36(8):879-894, July 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Guiding Trajectory Optimization by Demonstrated Distributions

Osa, T., Ghalamzan E., A. M., Stolkin, R., Lioutikov, R., Peters, J., Neumann, G.

IEEE Robotics and Automation Letters, 2(2):819-826, April 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
Whole-body multi-contact motion in humans and humanoids: Advances of the CoDyCo European project

Padois, V., Ivaldi, S., Babic, J., Mistry, M., Peters, J., Nori, F.

Robotics and Autonomous Systems, 90, pages: 97-117, April 2017, Special Issue on New Research Frontiers for Intelligent Autonomous Systems (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Probabilistic Movement Primitives for Coordination of Multiple Human-Robot Collaborative Tasks

Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O., Peters, J.

Autonomous Robots, 41(3):593-612, March 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Bioinspired tactile sensor for surface roughness discrimination

Yi, Z., Zhang, Y., Peters, J.

Sensors and Actuators A: Physical, 255, pages: 46-53, March 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills

Kupcsik, A., Deisenroth, M., Peters, J., Ai Poh, L., Vadakkepat, V., Neumann, G.

Artificial Intelligence, 247, pages: 415-439, 2017, Special Issue on AI and Robotics (article)

ei

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


no image
Anticipatory Action Selection for Human-Robot Table Tennis

Wang, Z., Boularias, A., Mülling, K., Schölkopf, B., Peters, J.

Artificial Intelligence, 247, pages: 399-414, 2017, Special Issue on AI and Robotics (article)

Abstract
Abstract Anticipation can enhance the capability of a robot in its interaction with humans, where the robot predicts the humans' intention for selecting its own action. We present a novel framework of anticipatory action selection for human-robot interaction, which is capable to handle nonlinear and stochastic human behaviors such as table tennis strokes and allows the robot to choose the optimal action based on prediction of the human partner's intention with uncertainty. The presented framework is generic and can be used in many human-robot interaction scenarios, for example, in navigation and human-robot co-manipulation. In this article, we conduct a case study on human-robot table tennis. Due to the limited amount of time for executing hitting movements, a robot usually needs to initiate its hitting movement before the opponent hits the ball, which requires the robot to be anticipatory based on visual observation of the opponent's movement. Previous work on Intention-Driven Dynamics Models (IDDM) allowed the robot to predict the intended target of the opponent. In this article, we address the problem of action selection and optimal timing for initiating a chosen action by formulating the anticipatory action selection as a Partially Observable Markov Decision Process (POMDP), where the transition and observation are modeled by the \{IDDM\} framework. We present two approaches to anticipatory action selection based on the \{POMDP\} formulation, i.e., a model-free policy learning method based on Least-Squares Policy Iteration (LSPI) that employs the \{IDDM\} for belief updates, and a model-based Monte-Carlo Planning (MCP) method, which benefits from the transition and observation model by the IDDM. Experimental results using real data in a simulated environment show the importance of anticipatory action selection, and that \{POMDPs\} are suitable to formulate the anticipatory action selection problem by taking into account the uncertainties in prediction. We also show that existing algorithms for POMDPs, such as \{LSPI\} and MCP, can be applied to substantially improve the robot's performance in its interaction with humans.

am ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
easyGWAS: A Cloud-based Platform for Comparing the Results of Genome-wide Association Studies

Grimm, D., Roqueiro, D., Salome, P., Kleeberger, S., Greshake, B., Zhu, W., Liu, C., Lippert, C., Stegle, O., Schölkopf, B., Weigel, D., Borgwardt, K.

The Plant Cell, 29(1):5-19, 2017 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation

Katiyar, P., Divine, M. R., Kohlhofer, U., Quintanilla-Martinez, L., Schölkopf, B., Pichler, B. J., Disselhorst, J. A.

Molecular Imaging and Biology, 19(3):391-397, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
Minimax Estimation of Kernel Mean Embeddings

Tolstikhin, I., Sriperumbudur, B., Muandet, K.

Journal of Machine Learning Research, 18(86):1-47, 2017 (article)

ei

link (url) Project Page [BibTex]


no image
Kernel Mean Embedding of Distributions: A Review and Beyond

Muandet, K., Fukumizu, K., Sriperumbudur, B., Schölkopf, B.

Foundations and Trends in Machine Learning, 10(1-2):1-141, 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Prediction of intention during interaction with iCub with Probabilistic Movement Primitives

Dermy, O., Paraschos, A., Ewerton, M., Charpillet, F., Peters, J., Ivaldi, S.

Frontiers in Robotics and AI, 4, pages: 45, 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Manifold-based multi-objective policy search with sample reuse

Parisi, S., Pirotta, M., Peters, J.

Neurocomputing, 263, pages: 3-14, (Editors: Madalina Drugan, Marco Wiering, Peter Vamplew, and Madhu Chetty), 2017, Special Issue on Multi-Objective Reinforcement Learning (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Robot Learning

Peters, J., Lee, D., Kober, J., Nguyen-Tuong, D., Bagnell, J., Schaal, S.

In Springer Handbook of Robotics, pages: 357-394, 15, 2nd, (Editors: Siciliano, Bruno and Khatib, Oussama), Springer International Publishing, 2017 (inbook)

am ei

Project Page [BibTex]

Project Page [BibTex]


no image
Spectral Clustering predicts tumor tissue heterogeneity using dynamic 18F-FDG PET: a complement to the standard compartmental modeling approach

Katiyar, P., Divine, M. R., Kohlhofer, U., Quintanilla-Martinez, L., Schölkopf, B., Pichler, B. J., Disselhorst, J. A.

Journal of Nuclear Medicine, 58(4):651-657, 2017 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Electroencephalographic identifiers of motor adaptation learning

Ozdenizci, O., Yalcin, M., Erdogan, A., Patoglu, V., Grosse-Wentrup, M., Cetin, M.

Journal of Neural Engineering, 14(4):046027, 2017 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Detecting distortions of peripherally presented letter stimuli under crowded conditions

Wallis, T. S. A., Tobias, S., Bethge, M., Wichmann, F. A.

Attention, Perception, & Psychophysics, 79(3):850-862, 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Policy Gradient Methods

Peters, J., Bagnell, J.

In Encyclopedia of Machine Learning and Data Mining, pages: 982-985, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (inbook)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


no image
Temporal evolution of the central fixation bias in scene viewing

Rothkegel, L. O. M., Trukenbrod, H. A., Schütt, H. H., Wichmann, F. A., Engbert, R.

Journal of Vision, 17(13):3, 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
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), pages: 151-167, Mathematics and Visualization, (Editors: Burch, M., Chuang, L., Fisher, B., Schmidt, A., and Weiskopf, D.), Springer, 2017 (inbook)

ei

DOI [BibTex]

DOI [BibTex]


no image
BundleMAP: Anatomically Localized Classification, Regression, and Hypothesis Testing in Diffusion MRI

Khatami, M., Schmidt-Wilcke, T., Sundgren, P. C., Abbasloo, A., Schölkopf, B., Schultz, T.

Pattern Recognition, 63, pages: 593-600, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
Statistical Asymmetries Between Cause and Effect

Janzing, D.

In Time in Physics, pages: 129-139, Tutorials, Schools, and Workshops in the Mathematical Sciences, (Editors: Renner, Renato and Stupar, Sandra), Springer International Publishing, Cham, 2017 (inbook)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
A parametric texture model based on deep convolutional features closely matches texture appearance for humans

Wallis, T. S. A., Funke, C. M., Ecker, A. S., Gatys, L. A., Wichmann, F. A., Bethge, M.

Journal of Vision, 17(12), 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Robot Learning

Peters, J., Tedrake, R., Roy, N., Morimoto, J.

In Encyclopedia of Machine Learning and Data Mining, pages: 1106-1109, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (inbook)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Model Selection for Gaussian Mixture Models

Huang, T., Peng, H., Zhang, K.

Statistica Sinica, 27(1):147-169, 2017 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
An image-computable psychophysical spatial vision model

Schütt, H. H., Wichmann, F. A.

Journal of Vision, 17(12), 2017 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Methods and measurements to compare men against machines

Wichmann, F. A., Janssen, D. H. J., Geirhos, R., Aguilar, G., Schütt, H. H., Maertens, M., Bethge, M.

Electronic Imaging, pages: 36-45(10), 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
A Comparison of Autoregressive Hidden Markov Models for Multimodal Manipulations With Variable Masses

Kroemer, O., Peters, J.

IEEE Robotics and Automation Letters, 2(2):1101-1108, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
Phase Estimation for Fast Action Recognition and Trajectory Generation in Human-Robot Collaboration

Maeda, G., Ewerton, M., Neumann, G., Lioutikov, R., Peters, J.

International Journal of Robotics Research, 36(13-14):1579-1594, 2017, Special Issue on the Seventeenth International Symposium on Robotics Research (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
A Phase-coded Aperture Camera with Programmable Optics

Chen, J., Hirsch, M., Heintzmann, R., Eberhardt, B., Lensch, H. P. A.

Electronic Imaging, 2017(17):70-75, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
On Maximum Entropy and Inference

Gresele, L., Marsili, M.

Entropy, 19(12):article no. 642, 2017 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Towards Engagement Models that Consider Individual Factors in HRI: On the Relation of Extroversion and Negative Attitude Towards Robots to Gaze and Speech During a Human-Robot Assembly Task

Ivaldi, S., Lefort, S., Peters, J., Chetouani, M., Provasi, J., Zibetti, E.

International Journal of Social Robotics, 9(1):63-86, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


no image
Non-parametric Policy Search with Limited Information Loss

van Hoof, H., Neumann, G., Peters, J.

Journal of Machine Learning Research , 18(73):1-46, 2017 (article)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


no image
Stability of Controllers for Gaussian Process Dynamics

Vinogradska, J., Bischoff, B., Nguyen-Tuong, D., Peters, J.

Journal of Machine Learning Research, 18(100):1-37, 2017 (article)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


no image
SUV-quantification of physiological lung tissue in an integrated PET/MR-system: Impact of lung density and bone tissue

Seith, F., Schmidt, H., Gatidis, S., Bezrukov, I., Schraml, C., Pfannenberg, C., la Fougère, C., Nikolaou, K., Schwenzer, N.

PLOS ONE, 12(5):1-13, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]

2013


no image
Correlation of Simultaneously Acquired Diffusion-Weighted Imaging and 2-Deoxy-[18F] fluoro-2-D-glucose Positron Emission Tomography of Pulmonary Lesions in a Dedicated Whole-Body Magnetic Resonance/Positron Emission Tomography System

Schmidt, H., Brendle, C., Schraml, C., Martirosian, P., Bezrukov, I., Hetzel, J., Müller, M., Sauter, A., Claussen, C., Pfannenberg, C., Schwenzer, N.

Investigative Radiology, 48(5):247-255, May 2013 (article)

ei

Web [BibTex]

2013


Web [BibTex]