1531 results (BibTeX)

2017


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Real-time Perception meets Reactive Motion Generation

Kappler, D., Meier, F., Issac, J., Mainprice, J., Cifuentes, C., Wüthrich, M., Berenz, V., Schaal, S., Ratliff, N., Bohg, J.

In Robotics: Science and Systems, 2017 (inproceedings) Submitted

Abstract
We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. Our approach emphasizes the importance of continuous, real-time perception and its tight integration with reactive motion generation methods. We present a fully integrated system where real-time object and robot tracking as well as ambient world modeling provides the necessary input to feedback controllers and continuous motion optimizers. Specifically, they provide attractive and repulsive potentials based on which the controllers and motion optimizer can online compute movement policies at different time intervals. We extensively evaluate the proposed system on a real robotic platform in four scenarios that exhibit either challenging workspace geometry or a dynamic environment. We compare the proposed integrated system with a more traditional sense-plan-act approach that is still widely used. In 333 experiments, we show the robustness and accuracy of the proposed system.

am

arxiv [BibTex]

2017


arxiv [BibTex]


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Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

Nestmeyer, T., Gehler, P.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

ps

pre-print Project Page [BibTex]

pre-print Project Page [BibTex]


Dynamic Time-of-Flight

Schober, M., Adam, A., Yair, O., Mazor, S., Nowozin, S.

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (conference) Accepted

ei pn

[BibTex]

[BibTex]


Discovering Causal Signals in Images

Lopez-Paz, D., Nishihara, R., Chintala, S., Schölkopf, B., Bottou, L.

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


Flexible Spatio-Temporal Networks for Video Prediction

Lu, C., Hirsch, M., Schölkopf, B.

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


Frequency Peak Features for Low-Channel Classification in Motor Imagery Paradigms

Jayaram, V., Schölkopf, B., Grosse-Wentrup, M.

Proceedings of the 8th International IEEE EMBS Conference on Neural Engineering (NER 2017), 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


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Learning Feedback Terms for Reactive Planning and Control

Rai, A., Sutanto, G., Schaal, S., Meier, F.

Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2017 (conference)

am

pdf video [BibTex]

pdf video [BibTex]


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Video Propagation Networks

Jampani, V., Gadde, R., Gehler, P.

In IEEE Conference on Computer Vision and Patter Recognition (CVPR), 2017 (inproceedings)

ps

arXiv Preprint [BibTex]

arXiv Preprint [BibTex]


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Detailed, accurate, human shape estimation from clothed 3D scan sequences

Zhang, C., Pujades, S., Black, M. J., Pons-Moll, G.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, Spotlight (inproceedings)

Abstract
We address the problem of estimating human body shape from 3D scans over time. Reliable estimation of 3D body shape is necessary for many applications including virtual try-on, health monitoring, and avatar creation for virtual reality. Scanning bodies in minimal clothing, however, presents a practical barrier to these applications. We address this problem by estimating body shape under clothing from a sequence of 3D scans. Previous methods that have exploited statistical models of body shape produce overly smooth shapes lacking personalized details. In this paper we contribute a new approach to recover not only an approximate shape of the person, but also their detailed shape. Our approach allows the estimated shape to deviate from a parametric model to fit the 3D scans. We demonstrate the method using high quality 4D data as well as sequences of visual hulls extracted from multi-view images. We also make available a new high quality 4D dataset that enables quantitative evaluation. Our method outperforms the previous state of the art, both qualitatively and quantitatively.

ps

arxiv_preprint [BibTex]

arxiv_preprint [BibTex]


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Dynamic FAUST: Registering Human Bodies in Motion

Bogo, F., Romero, J., Pons-Moll, G., Black, M. J.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, Oral (inproceedings)

ps

[BibTex]

coming soon [BibTex]


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Path Integral Guided Policy Search

Chebotar, Y., Kalakrishnan, M., Yahya, A., Li, A., Schaal, S., Levine, S.

Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), April 2017 (conference)

am

pdf video [BibTex]

pdf video [BibTex]


DeepCoder: Learning to Write Programs

Balog, M., Gaunt, A., Brockschmidt, M., Nowozin, S., Tarlow, D.

5th International Conference on Learning Representations (ICLR), 2017 (conference) Accepted

ei

Arxiv [BibTex]

Arxiv [BibTex]


Multi-frame blind image deconvolution through split frequency - phase recovery

Gauci, A., Abela, J., Cachia, E., Hirsch, M., ZarbAdami, K.

Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), pages: 1022511, (Editors: Yulin Wang, Tuan D. Pham, Vit Vozenilek, David Zhang, Yi Xie), 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


Memristor-based control methods for a bio-inspired robot

Tetzlaff, R., Ascoli, A., Baumann, D., Hild, M.

International Conference on Memristive Materials, Devices & Systems, April 2017 (conference) Accepted

am

[BibTex]

[BibTex]


Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

Klein, A., Falkner, S., Bartels, S., Hennig, P., Hutter, F.

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017), 52, JMLR Workshop and Conference Proceedings, (Editors: Sign, Aarti and Zhu, Jerry), 2017 (conference) Accepted

pn

[BibTex]

[BibTex]


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Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization

Marco, A., Berkenkamp, F., Hennig, P., Schoellig, A., Krause, A., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE International Conference on Robotics and Automation, May 2017 (inproceedings) Accepted

am pn

PDF [BibTex]

PDF [BibTex]


Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers

Doerr, A., Nguyen-Tuong, D., Marco, A., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2017 IEEE International Conference on Robotics and Automation, May 2017 (inproceedings) Accepted

am

PDF [BibTex]

PDF [BibTex]


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Distilling Information Reliability and Source Trustworthiness from Digital Traces

Tabibian, B., Valera, I., Farajtabar, M., Song, L., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the 26th International Conference on World Wide Web (WWW2017), 2017 (conference) Accepted

ei

Project [BibTex]

Project [BibTex]


DiSMEC – Distributed Sparse Machines for Extreme Multi-label Classification

Babbar, R., Schölkopf, B.

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM 2017), pages: 721-729, 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]

2016


Experimental and causal view on information integration in autonomous agents

Geiger, P., Hofmann, K., Schölkopf, B.

Proceedings of the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2016), pages: 21-28, (Editors: Hatzilygeroudis, I. and Palade, V.), 2016 (conference)

ei

link (url) [BibTex]

2016


link (url) [BibTex]


The Mondrian Kernel

Balog, M., Lakshminarayanan, B., Ghahramani, Z., Roy, D., Teh, Y.

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI), (Editors: Ihler, Alexander T. and Janzing, Dominik), 2016 (conference)

ei

Arxiv link (url) [BibTex]

Arxiv link (url) [BibTex]


Learning High-Order Filters for Efficient Blind Deconvolution of Document Photographs

Xiao, L., Wang, J., Heidrich, W., Hirsch, M.

Computer Vision - ECCV 2016, Lecture Notes in Computer Science, LNCS 9907, Part III, pages: 734-749, (Editors: Bastian Leibe, Jiri Matas, Nicu Sebe and Max Welling), Springer, 2016 (conference)

ei

DOI [BibTex]

DOI [BibTex]


Stepping stabilization using a combination of DCM tracking and step adjustment

Khadiv, M., Kleff, S., Herzog, A., Moosavian, S., Schaal, S., Righetti, L.

4th RSI International Conference on Robotics and Mechatronics, 2016 (conference)

am

arxiv [BibTex]

arxiv [BibTex]


A Convex Model of Humanoid Momentum Dynamics for Multi-Contact Motion Generation

Ponton, B., Herzog, A., Schaal, S., Righetti, L.

Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, 2016 (conference)

am

arxiv video [BibTex]

arxiv video [BibTex]


Balancing and Walking Using Full Dynamics LQR Control with Contact Constraints

Mason, S., rotella, N., Schaal, S., Righetti, L.

Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, 2016 (conference)

am

[BibTex]

[BibTex]


Step Timing Adjustment: A Step Toward Generating Robust Gaits

Khadiv, M., Herzog, A., Moosavian, S., Righetti, L.

Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, 2016 (conference)

am

arxiv video [BibTex]

arxiv video [BibTex]


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Generalizing Regrasping with Supervised Policy Learning

Chebotar, Y., Hausman, K., Kroemer, O., Sukhatme, G., Schaal, S.

In International Symposium on Experimental Robotics (ISER) 2016, International Symposium on Experimental Robotics, 2016 (inproceedings)

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pdf video [BibTex]

pdf video [BibTex]


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Self-Supervised Regrasping using Spatio-Temporal Tactile Features and Reinforcement Learning

Chebotar, Y., Hausman, K., Su, Z., Sukhatme, G., Schaal, S.

In International Conference on Intelligent Robots and Systems (IROS) 2016, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016 (inproceedings)

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pdf video [BibTex]

pdf video [BibTex]


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Dynamic baseline stereo vision-based cooperative target tracking

Ahmad, A., Ruff, E., Bülthoff, H.

In pages: 1728-1734, IEEE, 19th International Conference on Information Fusion (FUSION), 2016 (inproceedings)

Abstract
In this article we present a new method for multi-robot cooperative target tracking based on dynamic baseline stereo vision. The core novelty of our approach includes a computationally light-weight scheme to compute the 3D stereo measurements that exactly satisfy the epipolar constraints and a covariance intersection (CI)-based method to fuse the 3D measurements obtained by each individual robot. Using CI we are able to systematically integrate the robot localization uncertainties as well as the uncertainties in the measurements generated by the monocular camera images from each individual robot into the resulting stereo measurements. Through an extensive set of simulation and real robot results we show the robustness and accuracy of our approach with respect to ground truth. The source code related to this article is publicly accessible on our website and the datasets are available on request.

ps

[BibTex]

[BibTex]


Predictive and Self Triggering for Event-based State Estimation

Trimpe, S.

In Proceedings of the 55th IEEE Conference on Decision and Control, pages: 3098-3105, Las Vegas, NV, USA, December 2016 (inproceedings)

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arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


Event-based Sampling for Reducing Communication Load in Realtime Human Motion Analysis by Wireless Inertial Sensor Networks

Laidig, D., Trimpe, S., Seel, T.

In Current Directions in Biomedical Engineering, 2(1), 2016 (inproceedings)

am

PDF DOI [BibTex]

PDF DOI [BibTex]


Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

Tolstikhin, I., Sriperumbudur, B., Schölkopf, B.

Advances in Neural Information Processing Systems 29, 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference) Accepted

ei

[BibTex]

[BibTex]


Consistent Kernel Mean Estimation for Functions of Random Variables

Scibior, A., Simon-Gabriel, C., Tolstikhin, I., Schölkopf, B.

Advances in Neural Information Processing Systems 29, pages: 1732-1740, (Editors: D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R. Garnett), 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


End-to-End Learning for Image Burst Deblurring

Wieschollek, P., Schölkopf, B., Lensch, H., Hirsch, M.

Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, 2016 (conference) Accepted

ei

[BibTex]

[BibTex]


Multi-task logistic regression in brain-computer interfaces

Fiebig, K., Jayaram, V., Peters, J., Grosse-Wentrup, M.

Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), IEEE, 2016 (conference) To be published

ei

link (url) [BibTex]

link (url) [BibTex]


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Ensuring Ethical Behavior from Autonomous Systems

Anderson, M., Anderson, S., Berenz, V.

In Artificial Intelligence Applied to Assistive Technologies and Smart Environments, Papers from the 2016 AAAI Workshop, Phoenix, Arizona, USA, February 12, 2016, 2016 (inproceedings)

am

link (url) [BibTex]

link (url) [BibTex]


Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis

Huang, Y., Büchler, D., Koc, O., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots, Humanoids, 2016 (conference) Accepted

am ei

[BibTex]

[BibTex]


Using Probabilistic Movement Primitives for Striking Movements

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots, Humanoids, 2016 (conference) Accepted

am ei

[BibTex]

[BibTex]


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Deep Discrete Flow

Güney, F., Geiger, A.

Asian Conference on Computer Vision (ACCV), 2016 (conference) Accepted

avg ps

pdf suppmat [BibTex]

pdf suppmat [BibTex]


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Depth Estimation Through a Generative Model of Light Field Synthesis

Sajjadi, M., Köhler, R., Schölkopf, B., Hirsch, M.

Pattern Recognition: 38th German Conference, GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings, 9796, pages: 426-438, Lecture Notes in Computer Science, (Editors: Rosenhahn, B. and Andres, B.), Springer International Publishing, 2016 (conference)

ei

Arxiv link (url) DOI [BibTex]

Arxiv link (url) DOI [BibTex]


A New Trajectory Generation Framework in Robotic Table Tennis

Koc, O., Maeda, G., Peters, J.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IROS, 2016 (conference) Accepted

am ei

[BibTex]

[BibTex]


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Parameter Learning for Improving Binary Descriptor Matching

Sankaran, B., Ramalingam, S., Taguchi, Y.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IEEE, IROS, October 2016 (conference) Accepted

Abstract
Binary descriptors allow fast detection and matching algorithms in computer vision problems. Though binary descriptors can be computed at almost two orders of magnitude faster than traditional gradient based descriptors, they suffer from poor matching accuracy in challenging conditions. In this paper we propose three improvements for binary descriptors in their computation and matching that enhance their performance in comparison to traditional binary and non-binary descriptors without compromising their speed. This is achieved by learning some weights and threshold parameters that allow customized matching under some variations such as lighting and viewpoint. Our suggested improvements can be easily applied to any binary descriptor. We demonstrate our approach on the ORB (Oriented FAST and Rotated BRIEF) descriptor and compare its performance with the traditional ORB and SIFT descriptors on a wide variety of datasets. In all instances, our enhancements outperform standard ORB and is comparable to SIFT.

am

[BibTex]

[BibTex]


Active Nearest-Neighbor Learning in Metric Spaces

Kontorovich, A., Sabato, S., Urner, R.

Advances in Neural Information Processing Systems 29, 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference) Accepted

ei

[BibTex]

[BibTex]


Lifelong Learning with Weighted Majority Votes

Pentina, A., Urner, R.

Advances in Neural Information Processing Systems 29, 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference) Accepted

ei

[BibTex]

[BibTex]


Unsupervised clustering of EOG as a viable substitute for optical eye-tracking

Flad, N., Fomina, T., Bülthoff, H., Chuang, L.

First Workshop on Eye Tracking and Visualization (ETVIS 2015), (Editors: Weiskopf, D., Burch, M., Chuang, L., Fischer, B., and Schmidt, A.), Springer, 2016 (conference) In press

ei

[BibTex]

[BibTex]


Towards Robust Online Inverse Dynamics Learning

Meier, F., Kappler, D., Ratliff, N., Schaal, S.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IEEE, IROS, 2016 (conference) Accepted

am

fmeier_iros_2016 [BibTex]

fmeier_iros_2016 [BibTex]


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Learning Where to Search Using Visual Attention

Kloss, A., Kappler, D., Lensch, H., Butz, M., Schaal, S., Bohg, J.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IEEE, IROS, October 2016 (conference)

Abstract
One of the central tasks for a household robot is searching for specific objects. It does not only require localizing the target object but also identifying promising search locations in the scene if the target is not immediately visible. As computation time and hardware resources are usually limited in robotics, it is desirable to avoid expensive visual processing steps that are exhaustively applied over the entire image. The human visual system can quickly select those image locations that have to be processed in detail for a given task. This allows us to cope with huge amounts of information and to efficiently deploy the limited capacities of our visual system. In this paper, we therefore propose to use human fixation data to train a top-down saliency model that predicts relevant image locations when searching for specific objects. We show that the learned model can successfully prune bounding box proposals without rejecting the ground truth object locations. In this aspect, the proposed model outperforms a model that is trained only on the ground truth segmentations of the target object instead of fixation data.

am

Project Page [BibTex]

PDF Project Page [BibTex]


The Arrow of Time in Multivariate Time Serie

Bauer, S., Schölkopf, B., Peters, J.

Proceedings of the 33rd International Conference on Machine Learning, 48, pages: 2043-2051, JMLR Workshop and Conference Proceedings, (Editors: Balcan, M. F. and Weinberger, K. Q.), JMLR, ICML, 2016 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]