Header logo is


2014


Thumb xl fop
Human Pose Estimation with Fields of Parts

Kiefel, M., Gehler, P.

In Computer Vision – ECCV 2014, LNCS 8693, pages: 331-346, Lecture Notes in Computer Science, (Editors: Fleet, David and Pajdla, Tomas and Schiele, Bernt and Tuytelaars, Tinne), Springer, 13th European Conference on Computer Vision, September 2014 (inproceedings)

Abstract
This paper proposes a new formulation of the human pose estimation problem. We present the Fields of Parts model, a binary Conditional Random Field model designed to detect human body parts of articulated people in single images. The Fields of Parts model is inspired by the idea of Pictorial Structures, it models local appearance and joint spatial configuration of the human body. However the underlying graph structure is entirely different. The idea is simple: we model the presence and absence of a body part at every possible position, orientation, and scale in an image with a binary random variable. This results into a vast number of random variables, however, we show that approximate inference in this model is efficient. Moreover we can encode the very same appearance and spatial structure as in Pictorial Structures models. This approach allows us to combine ideas from segmentation and pose estimation into a single model. The Fields of Parts model can use evidence from the background, include local color information, and it is connected more densely than a kinematic chain structure. On the challenging Leeds Sports Poses dataset we improve over the Pictorial Structures counterpart by 5.5% in terms of Average Precision of Keypoints (APK).

ei ps

website pdf DOI Project Page [BibTex]

2014


website pdf DOI Project Page [BibTex]


Thumb xl ps page panel
Probabilistic Progress Bars

Kiefel, M., Schuler, C., Hennig, P.

In Conference on Pattern Recognition (GCPR), 8753, pages: 331-341, Lecture Notes in Computer Science, (Editors: Jiang, X., Hornegger, J., and Koch, R.), Springer, GCPR, September 2014 (inproceedings)

Abstract
Predicting the time at which the integral over a stochastic process reaches a target level is a value of interest in many applications. Often, such computations have to be made at low cost, in real time. As an intuitive example that captures many features of this problem class, we choose progress bars, a ubiquitous element of computer user interfaces. These predictors are usually based on simple point estimators, with no error modelling. This leads to fluctuating behaviour confusing to the user. It also does not provide a distribution prediction (risk values), which are crucial for many other application areas. We construct and empirically evaluate a fast, constant cost algorithm using a Gauss-Markov process model which provides more information to the user.

ei ps pn

website+code pdf DOI [BibTex]

website+code pdf DOI [BibTex]


no image
Seeing the Arrow of Time

Pickup, L., Zheng, P., Donglai, W., YiChang, S., Changshui, Z., Zisserman, A., Schölkopf, B., Freeman, W.

Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages: 2043-2050, IEEE, CVPR, June 2014 (conference)

ei

DOI [BibTex]

DOI [BibTex]


Thumb xl aistats2014
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics

Hennig, P., Hauberg, S.

In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics, 33, pages: 347-355, JMLR: Workshop and Conference Proceedings, (Editors: S Kaski and J Corander), Microtome Publishing, Brookline, MA, AISTATS, April 2014 (inproceedings)

Abstract
We study a probabilistic numerical method for the solution of both boundary and initial value problems that returns a joint Gaussian process posterior over the solution. Such methods have concrete value in the statistics on Riemannian manifolds, where non-analytic ordinary differential equations are involved in virtually all computations. The probabilistic formulation permits marginalising the uncertainty of the numerical solution such that statistics are less sensitive to inaccuracies. This leads to new Riemannian algorithms for mean value computations and principal geodesic analysis. Marginalisation also means results can be less precise than point estimates, enabling a noticeable speed-up over the state of the art. Our approach is an argument for a wider point that uncertainty caused by numerical calculations should be tracked throughout the pipeline of machine learning algorithms.

ei ps pn

pdf Youtube Supplements Project page link (url) [BibTex]

pdf Youtube Supplements Project page link (url) [BibTex]


no image
A Visual Analytics Approach to Study Anatomic Covariation

Hermann, M., Schunke, A., Schultz, T., Klein, R.

In Proceedings of IEEE Pacific Visualization 2014, pages: 161-168, March 2014 (inproceedings)

Abstract
Gaining insight into anatomic covariation helps the understanding of organismic shape variability in general and is of particular interest for delimiting morphological modules. Generation of hypotheses on structural covariation is undoubtedly a highly creative process, and as such, requires an exploratory approach. In this work we propose a new local anatomic covariance tensor which enables interactive visualizations to explore covariation at different levels of detail, stimulating rapid formation and (qualitative) evaluation of hypotheses. The effectiveness of the presented approach is demonstrated on a muCT dataset of mouse mandibles for which results from the literature are successfully reproduced, while providing a more detailed representation of covariation compared to state-of-the-art methods.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Multi-Task Feature Selection on Multiple Networks via Maximum Flows

Sugiyama, M., Azencott, C., Grimm, D., Kawahara, Y., Borgwardt, K.

In Proceedings of the 2014 SIAM International Conference on Data Mining , pages: 199-207, SIAM, 2014 (inproceedings)

ei

Web PDF DOI [BibTex]

Web PDF DOI [BibTex]


no image
Quantifying Information Overload in Social Media and its Impact on Social Contagions

Gomez Rodriguez, M., Gummadi, K., Schölkopf, B.

In Proceedings of the Eighth International Conference on Weblogs and Social Media, pages: 170-179, (Editors: E. Adar, P. Resnick, M. De Choudhury, B. Hogan, and A. Oh), AAAI Press, ICWSM, 2014 (inproceedings)

ei

Web [BibTex]

Web [BibTex]


no image
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm

Daneshmand, H., Gomez Rodriguez, M., Song, L., Schölkopf, B.

In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 793-801, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Interaction Primitives for Human-Robot Cooperation Tasks

Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., Peters, J.

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 2831-2837, IEEE, ICRA, 2014 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Learning to Predict Phases of Manipulation Tasks as Hidden States

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

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 4009-4014, IEEE, ICRA, 2014 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Visualizing Uncertainty in HARDI Tractography Using Superquadric Streamtubes

Wiens, V., Schlaffke, L., Schmidt-Wilcke, T., Schultz, T.

In Eurographics Conference on Visualization, Short Papers, (Editors: Elmqvist, N. and Hlawitschka, M. and Kennedy, J.), EuroVis, 2014 (inproceedings)

Abstract
Standard streamtubes for the visualization of diffusion MRI data are rendered either with a circular or with an elliptic cross section whose aspect ratio indicates the relative magnitudes of the medium and minor eigenvalues. Inspired by superquadric tensor glyphs, we propose to render streamtubes with a superquadric cross section, which develops sharp edges to more clearly convey the orientation of the second and third eigenvectors where they are uniquely defined, while maintaining a circular shape when the smaller two eigenvalues are equal. As a second contribution, we apply our novel superquadric streamtubes to visualize uncertainty in the tracking direction of HARDI tractography, which we represent using a novel propagation uncertainty tensor.

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
A Permutation-Based Kernel Conditional Independence Test

Doran, G., Muandet, K., Zhang, K., Schölkopf, B.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI2014), pages: 132-141, (Editors: Nevin L. Zhang and Jin Tian), AUAI Press Corvallis, Oregon, UAI2014, 2014 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
A unifying view of representer theorems

Argyriou, A., Dinuzzo, F.

In Proceedings of the 31th International Conference on Machine Learning, 32, pages: 748-756, (Editors: Xing, E. P. and Jebera, T.), ICML, 2014 (inproceedings)

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


no image
Riemannian Sparse Coding for Positive Definite Matrices

Cherian, A., Sra, S.

In 13th European Conference on Computer Vision, LNCS 8691, pages: 299-314, (Editors: Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T.), Springer, ECCV, 2014 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
Probabilistic ODE Solvers with Runge-Kutta Means

Schober, M., Duvenaud, D., Hennig, P.

In Advances in Neural Information Processing Systems 27, pages: 739-747, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

ei pn

Web link (url) [BibTex]

Web link (url) [BibTex]


no image
Mask-Specific Inpainting with Deep Neural Networks

Köhler, R., Schuler, C., Schölkopf, B., Harmeling, S.

In Pattern Recognition (GCPR 2014), pages: 523-534, (Editors: X Jiang, J Hornegger, and R Koch), Springer, 2014, Lecture Notes in Computer Science (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Randomized Nonlinear Component Analysis

Lopez-Paz, D., Sra, S., Smola, A., Ghahramani, Z., Schölkopf, B.

In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 1359-1367, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Causal and Anti-Causal Learning in Pattern Recognition for Neuroimaging

Weichwald, S., Schölkopf, B., Ball, T., Grosse-Wentrup, M.

In 4th International Workshop on Pattern Recognition in Neuroimaging (PRNI), IEEE , PRNI, 2014 (inproceedings)

ei

PDF Arxiv DOI [BibTex]

PDF Arxiv DOI [BibTex]


no image
Bayesian Gait Optimization for Bipedal Locomotion

Calandra, R., Gopalan, N., Seyfarth, A., Peters, J., Deisenroth, M.

In Proceedings of the 8th International Conference on Learning and Intelligent Optimization , LNCS 8426, pages: 274-290, Lecture Notes in Computer Science, (Editors: Pardalos, PM., Resende, MGC., Vogiatzis, C., and Walteros, JL.), Springer, LION, 2014 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Learning Manipulation by Sequencing Motor Primitives with a Two-Armed Robot

Lioutikov, R., Kroemer, O., Peters, J., Maeda, G.

In Proceedings of the 13th International Conference on Intelligent Autonomous Systems, 302, pages: 1601-1611, Advances in Intelligent Systems and Computing, (Editors: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H.), Springer, IAS, 2014 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
Kernel Mean Estimation via Spectral Filtering

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

In Advances in Neural Information Processing Systems 27, pages: 1-9, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

ei

Web link (url) [BibTex]

Web link (url) [BibTex]


no image
Consistency of Causal Inference under the Additive Noise Model

Kpotufe, S., Sgouritsa, E., Janzing, D., Schölkopf, B.

In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 478-495, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Active Learning of Linear Embeddings for Gaussian Processes

Garnett, R., Osborne, M., Hennig, P.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, pages: 230-239, (Editors: NL Zhang and J Tian), AUAI Press , Corvallis, Oregon, UAI2014, 2014, another link: http://arxiv.org/abs/1310.6740 (inproceedings)

ei pn

PDF Web [BibTex]

PDF Web [BibTex]


no image
Decoding Index Finger Position from EEG Using Random Forests

Weichwald, S., Meyer, T., Schölkopf, B., Ball, T., Grosse-Wentrup, M.

In 4th International Workshop on Cognitive Information Processing (CIP), IEEE, CIP, 2014 (inproceedings)

ei

PDF Arxiv DOI [BibTex]

PDF Arxiv DOI [BibTex]


no image
An Experimental Comparison of Bayesian Optimization for Bipedal Locomotion

Calandra, R., Seyfarth, A., Peters, J., Deisenroth, M.

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 1951-1958, IEEE, ICRA, 2014 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Multi-Task Policy Search for Robotics

Deisenroth, M., Englert, P., Peters, J., Fox, D.

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 3876-3881, IEEE, ICRA, 2014 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Sample-Based Information-Theoretic Stochastic Optimal Control

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

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 3896-3902, IEEE, ICRA, 2014 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers

Schober, M., Kasenburg, N., Feragen, A., Hennig, P., Hauberg, S.

In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Lecture Notes in Computer Science Vol. 8675, pages: 265-272, (Editors: P. Golland, N. Hata, C. Barillot, J. Hornegger and R. Howe), Springer, Heidelberg, MICCAI, 2014 (inproceedings)

ei pn

DOI [BibTex]

DOI [BibTex]


no image
Estimating Causal Effects by Bounding Confounding

Geiger, P., Janzing, D., Schölkopf, B.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence , pages: 240-249 , (Editors: Nevin L. Zhang and Jin Tian), AUAI Press Corvallis, Oregon , UAI, 2014 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Re-ranking Approach to Classification in Large-scale Power-law Distributed Category Systems

Babbar, R., Partalas, I., Gaussier, E., Amini, M.

In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages: 1059-1062, (Editors: S Geva and A Trotman and P Bruza and CLA Clarke and K Järvelin), ACM, New York, NY, USA, SIGIR, 2014 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
Kernel Mean Estimation and Stein Effect

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

In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 10-18, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Active Reward Learning

Daniel, C., Viering, M., Metz, J., Kroemer, O., Peters, J.

In Proceedings of Robotics: Science & Systems, (Editors: Fox, D., Kavraki, LE., and Kurniawati, H.), RSS, 2014 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Multi-modal filtering for non-linear estimation

Kamthe, S., Peters, J., Deisenroth, M.

In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pages: 7979-7983, IEEE, ICASSP, 2014 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Inferring latent structures via information inequalities

Chaves, R., Luft, L., Maciel, T., Gross, D., Janzing, D., Schölkopf, B.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, pages: 112-121, (Editors: NL Zhang and J Tian), AUAI Press, Corvallis, Oregon, UAI, 2014 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Policy Search For Learning Robot Control Using Sparse Data

Bischoff, B., Nguyen-Tuong, D., van Hoof, H., McHutchon, A., Rasmussen, C., Knoll, A., Peters, J., Deisenroth, M.

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 3882-3887, IEEE, ICRA, 2014 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Learning to Unscrew a Light Bulb from Demonstrations

Manschitz, S., Kober, J., Gienger, M., Peters, J.

In Proceedings for the joint conference of ISR 2014, 45th International Symposium on Robotics and Robotik 2014, 2014 (inproceedings)

ei

[BibTex]

[BibTex]


no image
Towards Neurofeedback Training of Associative Brain Areas for Stroke Rehabilitation

Özdenizci, O., Meyer, T., Cetin, M., Grosse-Wentrup, M.

In Proceedings of the 6th International Brain-Computer Interface Conference, (Editors: G Müller-Putz and G Bauernfeind and C Brunner and D Steyrl and S Wriessnegger and R Scherer), 2014 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature

Gunter, T., Osborne, M., Garnett, R., Hennig, P., Roberts, S.

In Advances in Neural Information Processing Systems 27, pages: 2789-2797, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

ei pn

Web link (url) [BibTex]

Web link (url) [BibTex]


no image
A Self-Tuning LQR Approach Demonstrated on an Inverted Pendulum

Trimpe, S., Millane, A., Doessegger, S., D’Andrea, R.

In Proceedings of the 19th IFAC World Congress, Cape Town, South Africa, 2014 (inproceedings)

am ics

PDF Supplementary material DOI [BibTex]

PDF Supplementary material DOI [BibTex]


no image
Fast Newton methods for the group fused lasso

Wytock, M., Sra, S., Kolter, J. Z.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, pages: 888-897, (Editors: Zhang, N. L. and Tian, J.), AUAI Press, UAI, 2014 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Efficient Structured Matrix Rank Minimization

Yu, A. W., Ma, W., Yu, Y., Carbonell, J., Sra, S.

Advances in Neural Information Processing Systems 27, pages: 1350-1358, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Towards building a Crowd-Sourced Sky Map

Lang, D., Hogg, D., Schölkopf, B.

In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, JMLR W\&CP 33, pages: 549–557, (Editors: S. Kaski and J. Corander), JMLR.org, AISTATS, 2014 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Incremental Local Gaussian Regression

Meier, F., Hennig, P., Schaal, S.

In Advances in Neural Information Processing Systems 27, pages: 972-980, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014, clmc (inproceedings)

am ei pn

PDF link (url) [BibTex]

PDF link (url) [BibTex]


no image
Learning to Deblur

Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B.

In NIPS 2014 Deep Learning and Representation Learning Workshop, 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Efficient Bayesian Local Model Learning for Control

Meier, F., Hennig, P., Schaal, S.

In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pages: 2244 - 2249, IROS, 2014, clmc (inproceedings)

Abstract
Model-based control is essential for compliant controland force control in many modern complex robots, like humanoidor disaster robots. Due to many unknown and hard tomodel nonlinearities, analytical models of such robots are oftenonly very rough approximations. However, modern optimizationcontrollers frequently depend on reasonably accurate models,and degrade greatly in robustness and performance if modelerrors are too large. For a long time, machine learning hasbeen expected to provide automatic empirical model synthesis,yet so far, research has only generated feasibility studies butno learning algorithms that run reliably on complex robots.In this paper, we combine two promising worlds of regressiontechniques to generate a more powerful regression learningsystem. On the one hand, locally weighted regression techniquesare computationally efficient, but hard to tune due to avariety of data dependent meta-parameters. On the other hand,Bayesian regression has rather automatic and robust methods toset learning parameters, but becomes quickly computationallyinfeasible for big and high-dimensional data sets. By reducingthe complexity of Bayesian regression in the spirit of local modellearning through variational approximations, we arrive at anovel algorithm that is computationally efficient and easy toinitialize for robust learning. Evaluations on several datasetsdemonstrate very good learning performance and the potentialfor a general regression learning tool for robotics.

am ei pn

PDF link (url) DOI [BibTex]

PDF link (url) DOI [BibTex]


no image
Stability Analysis of Distributed Event-Based State Estimation

Trimpe, S.

In Proceedings of the 53rd IEEE Conference on Decision and Control, Los Angeles, CA, 2014 (inproceedings)

Abstract
An approach for distributed and event-based state estimation that was proposed in previous work [1] is analyzed and extended to practical networked systems in this paper. Multiple sensor-actuator-agents observe a dynamic process, sporadically exchange their measurements over a broadcast network according to an event-based protocol, and estimate the process state from the received data. The event-based approach was shown in [1] to mimic a centralized Luenberger observer up to guaranteed bounds, under the assumption of identical estimates on all agents. This assumption, however, is unrealistic (it is violated by a single packet drop or slight numerical inaccuracy) and removed herein. By means of a simulation example, it is shown that non-identical estimates can actually destabilize the overall system. To achieve stability, the event-based communication scheme is supplemented by periodic (but infrequent) exchange of the agentsâ?? estimates and reset to their joint average. When the local estimates are used for feedback control, the stability guarantee for the estimation problem extends to the event-based control system.

am ics

PDF Supplementary material DOI Project Page [BibTex]

PDF Supplementary material DOI Project Page [BibTex]


no image
Towards an optimal stochastic alternating direction method of multipliers

Azadi, S., Sra, S.

Proceedings of the 31st International Conference on Machine Learning, 32, pages: 620-628, (Editors: Xing, E. P. and Jebara, T.), JMLR, ICML, 2014 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem

Gomez Rodriguez, M., Song, L., Schölkopf, B.

Proceedings of the 27th Conference on Learning Theory, 35, pages: 1276-1279, (Editors: Balcan, M.-F. and Szepesvári, C.), JMLR.org, COLT, 2014 (conference)

ei

PDF [BibTex]

PDF [BibTex]


no image
Dual Execution of Optimized Contact Interaction Trajectories

Toussaint, M., Ratliff, N., Bohg, J., Righetti, L., Englert, P., Schaal, S.

In 2014 IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 47-54, IEEE, Chicago, USA, 2014 (inproceedings)

Abstract
Efficient manipulation requires contact to reduce uncertainty. The manipulation literature refers to this as funneling: a methodology for increasing reliability and robustness by leveraging haptic feedback and control of environmental interaction. However, there is a fundamental gap between traditional approaches to trajectory optimization and this concept of robustness by funneling: traditional trajectory optimizers do not discover force feedback strategies. From a POMDP perspective, these behaviors could be regarded as explicit observation actions planned to sufficiently reduce uncertainty thereby enabling a task. While we are sympathetic to the full POMDP view, solving full continuous-space POMDPs in high-dimensions is hard. In this paper, we propose an alternative approach in which trajectory optimization objectives are augmented with new terms that reward uncertainty reduction through contacts, explicitly promoting funneling. This augmentation shifts the responsibility of robustness toward the actual execution of the optimized trajectories. Directly tracing trajectories through configuration space would lose all robustness-dual execution achieves robustness by devising force controllers to reproduce the temporal interaction profile encoded in the dual solution of the optimization problem. This work introduces dual execution in depth and analyze its performance through robustness experiments in both simulation and on a real-world robotic platform.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics

Herzog, A., Righetti, L., Grimminger, F., Pastor, P., Schaal, S.

In 2014 IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 981-988, IEEE, Chicago, USA, 2014 (inproceedings)

Abstract
Recently several hierarchical inverse dynamics controllers based on cascades of quadratic programs have been proposed for application on torque controlled robots. They have important theoretical benefits but have never been implemented on a torque controlled robot where model inaccuracies and real-time computation requirements can be problematic. In this contribution we present an experimental evaluation of these algorithms in the context of balance control for a humanoid robot. The presented experiments demonstrate the applicability of the approach under real robot conditions (i.e. model uncertainty, estimation errors, etc). We propose a simplification of the optimization problem that allows us to decrease computation time enough to implement it in a fast torque control loop. We implement a momentum-based balance controller which shows robust performance in face of unknown disturbances, even when the robot is standing on only one foot. In a second experiment, a tracking task is evaluated to demonstrate the performance of the controller with more complicated hierarchies. Our results show that hierarchical inverse dynamics controllers can be used for feedback control of humanoid robots and that momentum-based balance control can be efficiently implemented on a real robot.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]