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2019


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Color Constancy in Deep Neural Networks

Flachot, A., Schuett, H., Fleming, R. W., Wichmann, F. A., Gegenfurtner, K. R.

Journal of Vision, 19(10)(298), September 2019 (article)

Abstract
Journal of Vision 2019;19(10):298. doi: https://doi.org/10.1167/19.10.298.

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DOI [BibTex]

2019


DOI [BibTex]


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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)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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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]


Series Elastic Behavior of Biarticular Muscle-Tendon Structure in a Robotic Leg
Series Elastic Behavior of Biarticular Muscle-Tendon Structure in a Robotic Leg

Ruppert, F., Badri-Spröwitz, A.

Frontiers in Neurorobotics, 64, pages: 13, 13, August 2019 (article)

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Frontiers YouTube link (url) DOI [BibTex]

Frontiers YouTube link (url) DOI [BibTex]


Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics
Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics

Steve Heim, , Spröwitz, A.

IEEE Transactions on Robotics (T-RO) , 35(4), pages: 939-952, August 2019 (article)

Abstract
Properly designing a system to exhibit favorable natural dynamics can greatly simplify designing or learning the control policy. However, it is still unclear what constitutes favorable natural dynamics and how to quantify its effect. Most studies of simple walking and running models have focused on the basins of attraction of passive limit cycles and the notion of self-stability. We instead emphasize the importance of stepping beyond basins of attraction. In this paper, we show an approach based on viability theory to quantify robust sets in state-action space. These sets are valid for the family of all robust control policies, which allows us to quantify the robustness inherent to the natural dynamics before designing the control policy or specifying a control objective. We illustrate our formulation using spring-mass models, simple low-dimensional models of running systems. We then show an example application by optimizing robustness of a simulated planar monoped, using a gradient-free optimization scheme. Both case studies result in a nonlinear effective stiffness providing more robustness.

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arXiv preprint arXiv:1806.08081 T-RO link (url) DOI Project Page [BibTex]

arXiv preprint arXiv:1806.08081 T-RO link (url) DOI Project Page [BibTex]


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SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species

Miladinovic, D., Muheim, C., Bauer, S., Spinnler, A., Noain, D., Bandarabadi, M., Gallusser, B., Krummenacher, G., Baumann, C., Adamantidis, A., Brown, S. A., Buhmann, J. M.

PLOS Computational Biology, 15(4):1-30, Public Library of Science, April 2019 (article)

ei

DOI [BibTex]

DOI [BibTex]


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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)

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DOI [BibTex]

DOI [BibTex]


Multidimensional Contrast Limited Adaptive Histogram Equalization
Multidimensional Contrast Limited Adaptive Histogram Equalization

Stimper, V., Bauer, S., Ernstorfer, R., Schölkopf, B., Xian, R. P.

IEEE Access, 7, pages: 165437-165447, 2019 (article)

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arXiv link (url) DOI [BibTex]

arXiv link (url) DOI [BibTex]


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TD-regularized actor-critic methods

Parisi, S., Tangkaratt, V., Peters, J., Khan, M. E.

Machine Learning, 108(8):1467-1501, (Editors: Karsten Borgwardt, Po-Ling Loh, Evimaria Terzi, and Antti Ukkonen), 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Statistical Coverage Control of Mobile Sensor Networks

Arslan, Ö.

IEEE Transactions on Robotics, 35(4):889-908, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective

Tronarp, F., Kersting, H., Särkkä, S. H. P.

Statistics and Computing, 29(6):1297-1315, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots
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.

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Arxiv Video [BibTex]


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Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

Pfister*, N., Weichwald*, S., Bühlmann, P., Schölkopf, B.

Journal of Machine Learning Research, 20(147):1-50, 2019, *equal contribution (article)

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ArXiv Code Project page PDF link (url) Project Page Project Page [BibTex]

ArXiv Code Project page PDF link (url) Project Page Project Page [BibTex]


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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, 116(10):3988-3993, National Academy of Sciences, 2019 (article)

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link (url) DOI Project Page Project Page [BibTex]

link (url) DOI Project Page Project Page [BibTex]


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Entropic Regularization of Markov Decision Processes

Belousov, B., Peters, J.

Entropy, 21(7):674, 2019 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Searchers adjust their eye-movement dynamics to target characteristics in natural scenes

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

Scientific Reports, 9(1635), 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Spatial statistics for gaze patterns in scene viewing: Effects of repeated viewing

Trukenbrod, H. A., Barthelmé, S., Wichmann, F. A., Engbert, R.

Journal of Vision, 19(6):19, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Quantum mean embedding of probability distributions

Kübler, J. M., Muandet, K., Schölkopf, B.

Physical Review Research, 1(3):033159, American Physical Society, 2019 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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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., Munoz-Mari, J., van Nes, E., Peters, J., Quax, R., Reichstein, M., Scheffer, M., Schölkopf, B., Spirtes, P., Sugihara, G., Sun, J., Zhang, K., Zscheischler, J.

Nature Communications, 10(2553), 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Analysis of cause-effect inference by comparing regression errors

Blöbaum, P., Janzing, D., Washio, T., Shimizu, S., Schölkopf, B.

PeerJ Computer Science, 5, pages: e169, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Learning Intention Aware Online Adaptation of Movement Primitives

Koert, D., Pajarinen, J., Schotschneider, A., Trick, S., Rothkopf, C., Peters, J.

IEEE Robotics and Automation Letters, 4(4):3719-3726, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Spread-spectrum magnetic resonance imaging

Scheffler, K., Loktyushin, A., Bause, J., Aghaeifar, A., Steffen, T., Schölkopf, B.

Magnetic Resonance in Medicine, 82(3):877-885, 2019 (article)

ei

DOI [BibTex]

DOI [BibTex]


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How Cognitive Models of Human Body Experience Might Push Robotics

Schürmann, T., Mohler, B. J., Peters, J., Beckerle, P.

Frontiers in Neurorobotics, 13(14), 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Dense connectomic reconstruction in layer 4 of the somatosensory cortex

Motta, A., Berning, M., Boergens, K. M., Staffler, B., Beining, M., Loomba, S., Hennig, P., Wissler, H., Helmstaedter, M.

Science, 366(6469):eaay3134, American Association for the Advancement of Science, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Learning Trajectory Distributions for Assisted Teleoperation and Path Planning

Ewerton, M., Arenz, O., Maeda, G., Koert, D., Kolev, Z., Takahashi, M., Peters, J.

Frontiers in Robotics and AI, 6, pages: 89, 2019 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Brainglance: Visualizing Group Level MRI Data at One Glance

Stelzer, J., Lacosse, E., Bause, J., Scheffler, K., Lohmann, G.

Frontiers in Neuroscience, 13(972), 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces

Klus, S., Schuster, I., Muandet, K.

Journal of Nonlinear Science, 2019, First Online: 21 August 2019 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Workshops of the seventh international brain-computer interface meeting: not getting lost in translation

Huggins, J. E., Guger, C., Aarnoutse, E., Allison, B., Anderson, C. W., Bedrick, S., Besio, W., Chavarriaga, R., Collinger, J. L., Do, A. H., Herff, C., Hohmann, M., Kinsella, M., Lee, K., Lotte, F., Müller-Putz, G., Nijholt, A., Pels, E., Peters, B., Putze, F., Rupp, R. S. G., Scott, S., Tangermann, M., Tubig, P., Zander, T.

Brain-Computer Interfaces, 6(3):71-101, Taylor & Francis, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Compatible natural gradient policy search

Pajarinen, J., Thai, H. L., Akrour, R., Peters, J., Neumann, G.

Machine Learning, 108(8):1443-1466, (Editors: Karsten Borgwardt, Po-Ling Loh, Evimaria Terzi, and Antti Ukkonen), 2019 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Learning stable and predictive structures in kinetic systems

Pfister, N., Bauer, S., Peters, J.

Proceedings of the National Academy of Sciences (PNAS), 116(51):25405-25411, 2019 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Fairness Constraints: A Flexible Approach for Fair Classification

Zafar, M. B., Valera, I., Gomez-Rodriguez, M., Krishna, P.

Journal of Machine Learning Research, 20(75):1-42, 2019 (article)

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link (url) [BibTex]

link (url) [BibTex]

2017


Interactive Perception: Leveraging Action in Perception and Perception in Action
Interactive Perception: Leveraging Action in Perception and Perception in Action

Bohg, J., Hausman, K., Sankaran, B., Brock, O., Kragic, D., Schaal, S., Sukhatme, G.

IEEE Transactions on Robotics, 33, pages: 1273-1291, December 2017 (article)

Abstract
Recent approaches in robotics follow the insight that perception is facilitated by interactivity with the environment. These approaches are subsumed under the term of Interactive Perception (IP). We argue that IP provides the following benefits: (i) any type of forceful interaction with the environment creates a new type of informative sensory signal that would otherwise not be present and (ii) any prior knowledge about the nature of the interaction supports the interpretation of the signal. This is facilitated by knowledge of the regularity in the combined space of sensory information and action parameters. The goal of this survey is to postulate this as a principle and collect evidence in support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of Interactive Perception. We close this survey by discussing the remaining open questions. Thereby, we hope to define a field and inspire future work.

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arXiv DOI Project Page [BibTex]

2017


arXiv DOI Project Page [BibTex]


Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning

Li, W., Bohg, J., Fritz, M.

arXiv, November 2017 (article) Submitted

Abstract
Understanding physical phenomena is a key component of human intelligence and enables physical interaction with previously unseen environments. In this paper, we study how an artificial agent can autonomously acquire this intuition through interaction with the environment. We created a synthetic block stacking environment with physics simulation in which the agent can learn a policy end-to-end through trial and error. Thereby, we bypass to explicitly model physical knowledge within the policy. We are specifically interested in tasks that require the agent to reach a given goal state that may be different for every new trial. To this end, we propose a deep reinforcement learning framework that learns policies which are parametrized by a goal. We validated the model on a toy example navigating in a grid world with different target positions and in a block stacking task with different target structures of the final tower. In contrast to prior work, our policies show better generalization across different goals.

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arXiv [BibTex]


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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]

DOI Project Page [BibTex]


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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)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Event-based State Estimation: An Emulation-based Approach

Trimpe, S.

IET Control Theory & Applications, 11(11):1684-1693, July 2017 (article)

Abstract
An event-based state estimation approach for reducing communication in a networked control system is proposed. Multiple distributed sensor agents observe a dynamic process and sporadically transmit their measurements to estimator agents over a shared bus network. Local event-triggering protocols ensure that data is transmitted only when necessary to meet a desired estimation accuracy. The event-based design is shown to emulate the performance of a centralised state observer design up to guaranteed bounds, but with reduced communication. The stability results for state estimation are extended to the distributed control system that results when the local estimates are used for feedback control. Results from numerical simulations and hardware experiments illustrate the effectiveness of the proposed approach in reducing network communication.

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arXiv Supplementary material PDF DOI Project Page [BibTex]

arXiv Supplementary material PDF DOI Project Page [BibTex]


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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)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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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)

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DOI [BibTex]

DOI [BibTex]


Probabilistic Articulated Real-Time Tracking for Robot Manipulation
Probabilistic Articulated Real-Time Tracking for Robot Manipulation

(Best Paper of RA-L 2017, Finalist of Best Robotic Vision Paper Award of ICRA 2017)

Garcia Cifuentes, C., Issac, J., Wüthrich, M., Schaal, S., Bohg, J.

IEEE Robotics and Automation Letters (RA-L), 2(2):577-584, April 2017 (article)

Abstract
We propose a probabilistic filtering method which fuses joint measurements with depth images to yield a precise, real-time estimate of the end-effector pose in the camera frame. This avoids the need for frame transformations when using it in combination with visual object tracking methods. Precision is achieved by modeling and correcting biases in the joint measurements as well as inaccuracies in the robot model, such as poor extrinsic camera calibration. We make our method computationally efficient through a principled combination of Kalman filtering of the joint measurements and asynchronous depth-image updates based on the Coordinate Particle Filter. We quantitatively evaluate our approach on a dataset recorded from a real robotic platform, annotated with ground truth from a motion capture system. We show that our approach is robust and accurate even under challenging conditions such as fast motion, significant and long-term occlusions, and time-varying biases. We release the dataset along with open-source code of our approach to allow for quantitative comparison with alternative approaches.

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arXiv video code and dataset video PDF DOI Project Page [BibTex]


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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)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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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)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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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)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


Spinal joint compliance and actuation in a simulated bounding quadruped robot
Spinal joint compliance and actuation in a simulated bounding quadruped robot

Pouya, S., Khodabakhsh, M., Sproewitz, A., Ijspeert, A.

{Autonomous Robots}, pages: 437–452, Kluwer Academic Publishers, Springer, Dordrecht, New York, NY, Febuary 2017 (article)

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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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)

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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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.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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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]


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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]