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2014


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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]

2014


link (url) [BibTex]


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Learning coupling terms for obstacle avoidance

Rai, A., Meier, F., Ijspeert, A., Schaal, S.

In International Conference on Humanoid Robotics, pages: 512-518, IEEE, 2014, clmc (inproceedings)

Abstract
Autonomous manipulation in dynamic environments is important for robots to perform everyday tasks. For this, a manipulator should be capable of interpreting the environment and planning an appropriate movement. At least, two possible approaches exist for this in literature. Usually, a planning system is used to generate a complex movement plan that satisfies all constraints. Alternatively, a simple plan could be chosen and modified with sensory feedback to accommodate additional constraints by equipping the controller with features that remain dormant most of the time, except when specific situations arise. Dynamic Movement Primitives (DMPs) form a robust and versatile starting point for such a controller that can be modified online using a non-linear term, called the coupling term. This can prove to be a fast and reactive way of obstacle avoidance in a human-like fashion. We propose a method to learn this coupling term from human demonstrations starting with simple features and making it more robust to avoid a larger range of obstacles. We test the ability of our coupling term to model different kinds of obstacle avoidance behaviours in humans and use this learnt coupling term to avoid obstacles in a reactive manner. This line of research aims at pushing the boundary of reactive control strategies to more complex scenarios, such that complex and usually computationally more expensive planning methods can be avoided as much as possible.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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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]


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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]


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Active Microrheology of the Vitreous of the Eye applied to Nanorobot Propulsion

Qiu, T., Schamel, D., Mark, A. G., Fischer, P.

In 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), pages: 3801-3806, IEEE International Conference on Robotics and Automation ICRA, 2014, Best Automation Paper Award – Finalist. (inproceedings)

Abstract
Biomedical applications of micro or nanorobots require active movement through complex biological fluids. These are generally non-Newtonian (viscoelastic) fluids that are characterized by complicated networks of macromolecules that have size-dependent rheological properties. It has been suggested that an untethered microrobot could assist in retinal surgical procedures. To do this it must navigate the vitreous humor, a hydrated double network of collagen fibrils and high molecular-weight, polyanionic hyaluronan macromolecules. Here, we examine the characteristic size that potential robots must have to traverse vitreous relatively unhindered. We have constructed magnetic tweezers that provide a large gradient of up to 320 T/m to pull sub-micron paramagnetic beads through biological fluids. A novel two-step electrical discharge machining (EDM) approach is used to construct the tips of the magnetic tweezers with a resolution of 30 mu m and high aspect ratio of similar to 17:1 that restricts the magnetic field gradient to the plane of observation. We report measurements on porcine vitreous. In agreement with structural data and passive Brownian diffusion studies we find that the unhindered active propulsion through the eye calls for nanorobots with cross-sections of less than 500 nm.

Best Automation Paper Award – Finalist.

pf

[BibTex]

[BibTex]


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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]


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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]


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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]


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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]


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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]


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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]


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Three-dimensional robotic manipulation and transport of micro-scale objects by a magnetically driven capillary micro-gripper

Giltinan, J., Diller, E., Mayda, C., Sitti, M.

In Robotics and Automation (ICRA), 2014 IEEE International Conference on, pages: 2077-2082, 2014 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Increasing the sensor performance using Au modified high temperature superconducting YBa2Cu3O7-delta thin films

Katzer, C., Stahl, C., Michalowski, P., Treiber, S., Westernhausen, M., Schmidl, F., Seidel, P., Schütz, G., Albrecht, J.

In 507, IOP Pub., Genova, Italy, 2014 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Self-Exploration of the Stumpy Robot with Predictive Information Maximization

Martius, G., Jahn, L., Hauser, H., V. Hafner, V.

In Proc. From Animals to Animats, SAB 2014, 8575, pages: 32-42, LNCS, Springer, 2014 (inproceedings)

al

[BibTex]

[BibTex]


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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]


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Robotic assembly of hydrogels for tissue engineering and regenerative medicine

Tasoglu, S, Diller, E, Guven, S, Sitti, M, Demirci, U

In Journal of Tissue Engineering and Regenerative Medicine, 8, pages: 181-182, 2014 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Learning and Exploration in a Novel Dimensionality-Reduction Task

Ebert, J, Kim, S, Schweighofer, N., Sternad, D, Schaal, S.

In Abstracts of Neural Control of Movement Conference (NCM 2009), Amsterdam, Netherlands, 2014 (inproceedings)

am

[BibTex]

[BibTex]


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Versatile non-contact micro-manipulation method using rotational flows locally induced by magnetic microrobots

Ye, Z., Edington, C., Russell, A. J., Sitti, M.

In Advanced Intelligent Mechatronics (AIM), 2014 IEEE/ASME International Conference on, pages: 26-31, 2014 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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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]


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Full Dynamics LQR Control of a Humanoid Robot: An Experimental Study on Balancing and Squatting

Mason, S., Righetti, L., Schaal, S.

In 2014 IEEE-RAS International Conference on Humanoid Robots, pages: 374-379, IEEE, Madrid, Spain, 2014 (inproceedings)

Abstract
Humanoid robots operating in human environments require whole-body controllers that can offer precise tracking and well-defined disturbance rejection behavior. In this contribution, we propose an experimental evaluation of a linear quadratic regulator (LQR) using a linearization of the full robot dynamics together with the contact constraints. The advantage of the controller is that it explicitly takes into account the coupling between the different joints to create optimal feedback controllers for whole-body control. We also propose a method to explicitly regulate other tasks of interest, such as the regulation of the center of mass of the robot or its angular momentum. In order to evaluate the performance of linear optimal control designs in a real-world scenario (model uncertainty, sensor noise, imperfect state estimation, etc), we test the controllers in a variety of tracking and balancing experiments on a torque controlled humanoid (e.g. balancing, split plane balancing, squatting, pushes while squatting, and balancing on a wheeled platform). The proposed control framework shows a reliable push recovery behavior competitive with more sophisticated balance controllers, rejecting impulses up to 11.7 Ns with peak forces of 650 N, with the added advantage of great computational simplicity. Furthermore, the controller is able to track squatting trajectories up to 1 Hz without relinearization, suggesting that the linearized dynamics is sufficient for significant ranges of motion.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Curiosity-driven learning with Context Tree Weighting

Peng, Z, Braun, DA

pages: 366-367, IEEE, Piscataway, NJ, USA, 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (IEEE ICDL-EPIROB), October 2014 (conference)

Abstract
In the first simulation, the intrinsic motivation of the agent was given by measuring learning progress through reduction in informational surprise (Figure 1 A-C). This way the agent should first learn the action that is easiest to learn (a1), and then switch to other actions that still allow for learning (a2) and ignore actions that cannot be learned at all (a3). This is exactly what we found in our simple environment. Compared to the original developmental learning algorithm based on learning progress proposed by Oudeyer [2], our Context Tree Weighting approach does not require local experts to do prediction, rather it learns the conditional probability distribution over observations given action in one structure. In the second simulation, the intrinsic motivation of the agent was given by measuring compression progress through improvement in compressibility (Figure 1 D-F). The agent behaves similarly: the agent first concentrates on the action with the most predictable consequence and then switches over to the regular action where the consequence is more difficult to predict, but still learnable. Unlike the previous simulation, random actions are also interesting to some extent because the compressed symbol strings use 8-bit representations, while only 2 bits are required for our observation space. Our preliminary results suggest that Context Tree Weighting might provide a useful representation to study problems of development.

ei

DOI [BibTex]

DOI [BibTex]


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Structural optimization method towards synthesis of small scale flexure-based mobile grippers

Lum, G. Z., Diller, E., Sitti, M.

In Robotics and Automation (ICRA), 2014 IEEE International Conference on, pages: 2339-2344, 2014 (inproceedings)

pi

[BibTex]

[BibTex]


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Six-Degrees-of-Freedom Remote Actuation of Magnetic Microrobots.

Diller, E. D., Giltinan, J., Lum, G. Z., Ye, Z., Sitti, M.

In Robotics: Science and Systems, 2014 (inproceedings)

pi

[BibTex]

[BibTex]


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State Estimation for a Humanoid Robot

Rotella, N., Bloesch, M., Righetti, L., Schaal, S.

In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 952-958, IEEE, Chicago, USA, 2014 (inproceedings)

Abstract
This paper introduces a framework for state estimation on a humanoid robot platform using only common proprioceptive sensors and knowledge of leg kinematics. The presented approach extends that detailed in prior work on a point-foot quadruped platform by adding the rotational constraints imposed by the humanoid's flat feet. As in previous work, the proposed Extended Kalman Filter accommodates contact switching and makes no assumptions about gait or terrain, making it applicable on any humanoid platform for use in any task. A nonlinear observability analysis is performed on both the point-foot and flat-foot filters and it is concluded that the addition of rotational constraints significantly simplifies singular cases and improves the observability characteristics of the system. Results on a simulated walking dataset demonstrate the performance gain of the flat-foot filter as well as confirm the results of the presented observability analysis.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Monte Carlo methods for exact & efficient solution of the generalized optimality equations

Ortega, PA, Braun, DA, Tishby, N

pages: 4322-4327, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), June 2014 (conference)

Abstract
Previous work has shown that classical sequential decision making rules, including expectimax and minimax, are limit cases of a more general class of bounded rational planning problems that trade off the value and the complexity of the solution, as measured by its information divergence from a given reference. This allows modeling a range of novel planning problems having varying degrees of control due to resource constraints, risk-sensitivity, trust and model uncertainty. However, so far it has been unclear in what sense information constraints relate to the complexity of planning. In this paper, we introduce Monte Carlo methods to solve the generalized optimality equations in an efficient \& exact way when the inverse temperatures in a generalized decision tree are of the same sign. These methods highlight a fundamental relation between inverse temperatures and the number of Monte Carlo proposals. In particular, it is seen that the number of proposals is essentially independent of the size of the decision tree.

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]

2012


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Towards Multi-DOF model mediated teleoperation: Using vision to augment feedback

Willaert, B., Bohg, J., Van Brussel, H., Niemeyer, G.

In IEEE International Workshop on Haptic Audio Visual Environments and Games (HAVE), pages: 25-31, October 2012 (inproceedings)

Abstract
In this paper, we address some of the challenges that arise as model-mediated teleoperation is applied to systems with multiple degrees of freedom and multiple sensors. Specifically we use a system with position, force, and vision sensors to explore an environment geometry in two degrees of freedom. The inclusion of vision is proposed to alleviate the difficulties of estimating an increasing number of environment properties. Vision can furthermore increase the predictive nature of model-mediated teleoperation, by effectively predicting touch feedback before the slave is even in contact with the environment. We focus on the case of estimating the location and orientation of a local surface patch at the contact point between the slave and the environment. We describe the various information sources with their respective limitations and create a combined model estimator as part of a multi-d.o.f. model-mediated controller. An experiment demonstrates the feasibility and benefits of utilizing vision sensors in teleoperation.

am

DOI [BibTex]

2012


DOI [BibTex]


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Failure Recovery with Shared Autonomy

Sankaran, B., Pitzer, B., Osentoski, S.

In International Conference on Intelligent Robots and Systems, October 2012 (inproceedings)

Abstract
Building robots capable of long term autonomy has been a long standing goal of robotics research. Such systems must be capable of performing certain tasks with a high degree of robustness and repeatability. In the context of personal robotics, these tasks could range anywhere from retrieving items from a refrigerator, loading a dishwasher, to setting up a dinner table. Given the complexity of tasks there are a multitude of failure scenarios that the robot can encounter, irrespective of whether the environment is static or dynamic. For a robot to be successful in such situations, it would need to know how to recover from failures or when to ask a human for help. This paper, presents a novel shared autonomy behavioral executive to addresses these issues. We demonstrate how this executive combines generalized logic based recovery and human intervention to achieve continuous failure free operation. We tested the systems over 250 trials of two different use case experiments. Our current algorithm drastically reduced human intervention from 26% to 4% on the first experiment and 46% to 9% on the second experiment. This system provides a new dimension to robot autonomy, where robots can exhibit long term failure free operation with minimal human supervision. We also discuss how the system can be generalized.

am

link (url) [BibTex]

link (url) [BibTex]


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Lie Bodies: A Manifold Representation of 3D Human Shape

Freifeld, O., Black, M. J.

In European Conf. on Computer Vision (ECCV), pages: 1-14, Part I, LNCS 7572, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

Abstract
Three-dimensional object shape is commonly represented in terms of deformations of a triangular mesh from an exemplar shape. Existing models, however, are based on a Euclidean representation of shape deformations. In contrast, we argue that shape has a manifold structure: For example, summing the shape deformations for two people does not necessarily yield a deformation corresponding to a valid human shape, nor does the Euclidean difference of these two deformations provide a meaningful measure of shape dissimilarity. Consequently, we define a novel manifold for shape representation, with emphasis on body shapes, using a new Lie group of deformations. This has several advantages. First we define triangle deformations exactly, removing non-physical deformations and redundant degrees of freedom common to previous methods. Second, the Riemannian structure of Lie Bodies enables a more meaningful definition of body shape similarity by measuring distance between bodies on the manifold of body shape deformations. Third, the group structure allows the valid composition of deformations. This is important for models that factor body shape deformations into multiple causes or represent shape as a linear combination of basis shapes. Finally, body shape variation is modeled using statistics on manifolds. Instead of modeling Euclidean shape variation with Principal Component Analysis we capture shape variation on the manifold using Principal Geodesic Analysis. Our experiments show consistent visual and quantitative advantages of Lie Bodies over traditional Euclidean models of shape deformation and our representation can be easily incorporated into existing methods.

ps

pdf supplemental material youtube poster eigenshape video code Project Page Project Page Project Page [BibTex]

pdf supplemental material youtube poster eigenshape video code Project Page Project Page Project Page [BibTex]


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Coregistration: Simultaneous alignment and modeling of articulated 3D shape

Hirshberg, D., Loper, M., Rachlin, E., Black, M.

In European Conf. on Computer Vision (ECCV), pages: 242-255, LNCS 7577, Part IV, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

Abstract
Three-dimensional (3D) shape models are powerful because they enable the inference of object shape from incomplete, noisy, or ambiguous 2D or 3D data. For example, realistic parameterized 3D human body models have been used to infer the shape and pose of people from images. To train such models, a corpus of 3D body scans is typically brought into registration by aligning a common 3D human-shaped template to each scan. This is an ill-posed problem that typically involves solving an optimization problem with regularization terms that penalize implausible deformations of the template. When aligning a corpus, however, we can do better than generic regularization. If we have a model of how the template can deform then alignments can be regularized by this model. Constructing a model of deformations, however, requires having a corpus that is already registered. We address this chicken-and-egg problem by approaching modeling and registration together. By minimizing a single objective function, we reliably obtain high quality registration of noisy, incomplete, laser scans, while simultaneously learning a highly realistic articulated body model. The model greatly improves robustness to noise and missing data. Since the model explains a corpus of body scans, it captures how body shape varies across people and poses.

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pdf publisher site poster supplemental material (400MB) Project Page Project Page [BibTex]

pdf publisher site poster supplemental material (400MB) Project Page Project Page [BibTex]


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Lessons and insights from creating a synthetic optical flow benchmark

Wulff, J., Butler, D. J., Stanley, G. B., Black, M. J.

In ECCV Workshop on Unsolved Problems in Optical Flow and Stereo Estimation, pages: 168-177, Part II, LNCS 7584, (Editors: A. Fusiello et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

ps

pdf dataset poster youtube Project Page [BibTex]

pdf dataset poster youtube Project Page [BibTex]


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3D2PM – 3D Deformable Part Models

Pepik, B., Gehler, P., Stark, M., Schiele, B.

In Proceedings of the European Conference on Computer Vision (ECCV), pages: 356-370, Lecture Notes in Computer Science, (Editors: Fitzgibbon, Andrew W. and Lazebnik, Svetlana and Perona, Pietro and Sato, Yoichi and Schmid, Cordelia), Springer, Firenze, October 2012 (inproceedings)

ps

pdf video poster Project Page [BibTex]

pdf video poster Project Page [BibTex]


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A naturalistic open source movie for optical flow evaluation

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J.

In European Conf. on Computer Vision (ECCV), pages: 611-625, Part IV, LNCS 7577, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

Abstract
Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the image- and flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.

ps

pdf dataset youtube talk supplemental material Project Page Project Page [BibTex]

pdf dataset youtube talk supplemental material Project Page Project Page [BibTex]


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Task-Based Grasp Adaptation on a Humanoid Robot

Bohg, J., Welke, K., León, B., Do, M., Song, D., Wohlkinger, W., Aldoma, A., Madry, M., Przybylski, M., Asfour, T., Marti, H., Kragic, D., Morales, A., Vincze, M.

In 10th IFAC Symposium on Robot Control, SyRoCo 2012, Dubrovnik, Croatia, September 5-7, 2012., pages: 779-786, September 2012 (inproceedings)

Abstract
In this paper, we present an approach towards autonomous grasping of objects according to their category and a given task. Recent advances in the field of object segmentation and categorization as well as task-based grasp inference have been leveraged by integrating them into one pipeline. This allows us to transfer task-specific grasp experience between objects of the same category. The effectiveness of the approach is demonstrated on the humanoid robot ARMAR-IIIa.

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Video pdf DOI [BibTex]

Video pdf DOI [BibTex]


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A framework for relating neural activity to freely moving behavior

Foster, J. D., Nuyujukian, P., Freifeld, O., Ryu, S., Black, M. J., Shenoy, K. V.

In 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’12), pages: 2736 -2739 , IEEE, San Diego, August 2012 (inproceedings)

ps

pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Pottics – The Potts Topic Model for Semantic Image Segmentation

Dann, C., Gehler, P., Roth, S., Nowozin, S.

In Proceedings of 34th DAGM Symposium, pages: 397-407, Lecture Notes in Computer Science, (Editors: Pinz, Axel and Pock, Thomas and Bischof, Horst and Leberl, Franz), Springer, August 2012 (inproceedings)

ps

code pdf poster [BibTex]

code pdf poster [BibTex]


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Influence Maximization in Continuous Time Diffusion Networks

Gomez Rodriguez, M., Schölkopf, B.

In Proceedings of the 29th International Conference on Machine Learning, pages: 313-320, (Editors: J, Langford and J, Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

ei

Web [BibTex]

Web [BibTex]


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Submodular Inference of Diffusion Networks from Multiple Trees

Gomez Rodriguez, M., Schölkopf, B.

In Proceedings of the 29th International Conference on Machine Learning , pages: 489-496, (Editors: J Langford, and J Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

ei

Web [BibTex]

Web [BibTex]


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Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

In Proceedings of the 29th International Conference on Machine Learning, pages: 25-32, ICML ’12, (Editors: John Langford and Joelle Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

Abstract
Four decades after their invention, quasi- Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.

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website+code pdf link (url) [BibTex]

website+code pdf link (url) [BibTex]


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Image denoising: Can plain Neural Networks compete with BM3D?

Burger, H., Schuler, C., Harmeling, S.

In pages: 2392 - 2399, 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2012 (inproceedings)

Abstract
Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. The best currently available denoising methods approximate this mapping with cleverly engineered algorithms. In this work we attempt to learn this mapping directly with a plain multi layer perceptron (MLP) applied to image patches. While this has been done before, we will show that by training on large image databases we are able to compete with the current state-of-the-art image denoising methods. Furthermore, our approach is easily adapted to less extensively studied types of noise (by merely exchanging the training data), for which we achieve excellent results as well.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Climate classifications: the value of unsupervised clustering

Zscheischler, J., Mahecha, M., Harmeling, S.

In Proceedings of the International Conference on Computational Science , 9, pages: 897-906, Procedia Computer Science, (Editors: H. Ali, Y. Shi, D. Khazanchi, M. Lees, G.D. van Albada, J. Dongarra, P.M.A. Sloot, J. Dongarra), Elsevier, Amsterdam, Netherlands, ICCS, June 2012 (inproceedings)

Abstract
Classifying the land surface according to di erent climate zones is often a prerequisite for global diagnostic or predictive modelling studies. Classical classifications such as the prominent K¨oppen–Geiger (KG) approach rely on heuristic decision rules. Although these heuristics may transport some process understanding, such a discretization may appear “arbitrary” from a data oriented perspective. In this contribution we compare the precision of a KG classification to an unsupervised classification (k-means clustering). Generally speaking, we revisit the problem of “climate classification” by investigating the inherent patterns in multiple data streams in a purely data driven way. One question is whether we can reproduce the KG boundaries by exploring di erent combinations of climate and remotely sensed vegetation variables. In this context we also investigate whether climate and vegetation variables build similar clusters. In terms of statistical performances, k-means clearly outperforms classical climate classifications. However, a subsequent stability analysis only reveals a meaningful number of clusters if both climate and vegetation data are considered in the analysis. This is a setback for the hope to explain vegetation by means of climate alone. Clearly, classification schemes like K¨oppen-Geiger will play an important role in the future. However, future developments in this area need to be assessed based on data driven approaches.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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From pictorial structures to deformable structures

Zuffi, S., Freifeld, O., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3546-3553, IEEE, June 2012 (inproceedings)

Abstract
Pictorial Structures (PS) define a probabilistic model of 2D articulated objects in images. Typical PS models assume an object can be represented by a set of rigid parts connected with pairwise constraints that define the prior probability of part configurations. These models are widely used to represent non-rigid articulated objects such as humans and animals despite the fact that such objects have parts that deform non-rigidly. Here we define a new Deformable Structures (DS) model that is a natural extension of previous PS models and that captures the non-rigid shape deformation of the parts. Each part in a DS model is represented by a low-dimensional shape deformation space and pairwise potentials between parts capture how the shape varies with pose and the shape of neighboring parts. A key advantage of such a model is that it more accurately models object boundaries. This enables image likelihood models that are more discriminative than previous PS likelihoods. This likelihood is learned using training imagery annotated using a DS “puppet.” We focus on a human DS model learned from 2D projections of a realistic 3D human body model and use it to infer human poses in images using a form of non-parametric belief propagation.

ps

pdf sup mat code poster Project Page Project Page Project Page Project Page [BibTex]

pdf sup mat code poster Project Page Project Page Project Page Project Page [BibTex]


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Teaching 3D Geometry to Deformable Part Models

Pepik, B., Stark, M., Gehler, P., Schiele, B.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 3362 -3369, IEEE, Providence, RI, USA, June 2012, oral presentation (inproceedings)

ps

pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Learning Tracking Control with Forward Models

Bócsi, B., Hennig, P., Csató, L., Peters, J.

In pages: 259 -264, IEEE International Conference on Robotics and Automation (ICRA), May 2012 (inproceedings)

Abstract
Performing task-space tracking control on redundant robot manipulators is a difficult problem. When the physical model of the robot is too complex or not available, standard methods fail and machine learning algorithms can have advantages. We propose an adaptive learning algorithm for tracking control of underactuated or non-rigid robots where the physical model of the robot is unavailable. The control method is based on the fact that forward models are relatively straightforward to learn and local inversions can be obtained via local optimization. We use sparse online Gaussian process inference to obtain a flexible probabilistic forward model and second order optimization to find the inverse mapping. Physical experiments indicate that this approach can outperform state-of-the-art tracking control algorithms in this context.

ei pn

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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A Kernel-based Approach to Direct Action Perception

Kroemer, O., Ugur, E., Oztop, E., Peters, J.

In International Conference on Robotics and Automation (ICRA 2012), pages: 2605-2610, IEEE, IEEE International Conference on Robotics and Automation (ICRA), May 2012 (inproceedings)

Abstract
The direct perception of actions allows a robot to predict the afforded actions of observed novel objects. In addition to learning which actions are afforded, the robot must also learn to adapt its actions according to the object being manipulated. In this paper, we present a non-parametric approach to representing the affordance-bearing subparts of objects. This representation forms the basis of a kernel function for computing the similarity between different subparts. Using this kernel function, the robot can learn the required mappings to perform direct action perception. The proposed approach was successfully implemented on a real robot, which could then quickly learn to generalize grasping and pouring actions to novel objects.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Accelerating Nearest Neighbor Search on Manycore Systems

Cayton, L.

In Parallel Distributed Processing Symposium (IPDPS), 2012 IEEE 26th International, pages: 402-413, IPDPS, May 2012 (inproceedings)

Abstract
We develop methods for accelerating metric similarity search that are effective on modern hardware. Our algorithms factor into easily parallelizable components, making them simple to deploy and efficient on multicore CPUs and GPUs. Despite the simple structure of our algorithms, their search performance is provably sublinear in the size of the database, with a factor dependent only on its intrinsic dimensionality. We demonstrate that our methods provide substantial speedups on a range of datasets and hardware platforms. In particular, we present results on a 48-core server machine, on graphics hardware, and on a multicore desktop.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits

Seldin, Y., Cesa-Bianchi, N., Auer, P., Laviolette, F., Shawe-Taylor, J.

In JMLR Workshop and Conference Proceedings 26, pages: 98-111, JMLR, Cambridge, MA, USA, On-line Trading of Exploration and Exploitation 2, April 2012 (inproceedings)

Abstract
We develop a new tool for data-dependent analysis of the exploration-exploitation trade-off in learning under limited feedback. Our tool is based on two main ingredients. The first ingredient is a new concentration inequality that makes it possible to control the concentration of weighted averages of multiple (possibly uncountably many) simultaneously evolving and interdependent martingales. The second ingredient is an application of this inequality to the exploration-exploitation trade-off via importance weighted sampling. We apply the new tool to the stochastic multiarmed bandit problem, however, the main importance of this paper is the development and understanding of the new tool rather than improvement of existing algorithms for stochastic multiarmed bandits. In the follow-up work we demonstrate that the new tool can improve over state-of-the-art in structurally richer problems, such as stochastic multiarmed bandits with side information (Seldin et al., 2011a).

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Hierarchical Relative Entropy Policy Search

Daniel, C., Neumann, G., Peters, J.

In Fifteenth International Conference on Artificial Intelligence and Statistics, 22, pages: 273-281, JMLR Proceedings, (Editors: Lawrence, N. D. and Girolami, M.), JMLR.org, AISTATS, April 2012 (inproceedings)

Abstract
Many real-world problems are inherently hierarchically structured. The use of this structure in an agent's policy may well be the key to improved scalability and higher performance. However, such hierarchical structures cannot be exploited by current policy search algorithms. We will concentrate on a basic, but highly relevant hierarchy - the `mixed option' policy. Here, a gating network fi rst decides which of the options to execute and, subsequently, the option-policy determines the action. In this paper, we reformulate learning a hierarchical policy as a latent variable estimation problem and subsequently extend the Relative Entropy Policy Search (REPS) to the latent variable case. We show that our Hierarchical REPS can learn versatile solutions while also showing an increased performance in terms of learning speed and quality of the found policy in comparison to the nonhierarchical approach.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Movement Segmentation and Recognition for Imitation Learning

Meier, F., Theodorou, E., Schaal, S.

In Seventeenth International Conference on Artificial Intelligence and Statistics, La Palma, Canary Islands, Fifteenth International Conference on Artificial Intelligence and Statistics , April 2012 (inproceedings)

am

link (url) [BibTex]

link (url) [BibTex]


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Personalized medicine: from genotypes and molecular phenotypes towards computed therapy

Stegle, O., Roth, FP., Morris, Q., Listgarten, J.

In pages: 323-326, (Editors: Altman, R.B. , A.K. Dunker, L. Hunter, T. Murray, T.E. Klein), World Scientific Publishing, Singapore, Pacific Symposium on Biocomputing (PSB), January 2012 (inproceedings)

Abstract
Joint genotyping and large-scale phenotyping of molecular traits are currently available for a number of important patient study cohorts and will soon become feasible in routine medical practice. These data are one component of several that are setting the stage for the development of personalized medicine, promising to yield better disease classification, enabling more specific treatment, and also allowing for improved preventive medical screening. This conference session explores statistical challenges and new opportunities that arise from application of genome-scale experimentation for personalized genomics and medicine.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Approximate Gaussian Integration using Expectation Propagation

Cunningham, J., Hennig, P., Lacoste-Julien, S.

In pages: 1-11, -, January 2012 (inproceedings) Submitted

Abstract
While Gaussian probability densities are omnipresent in applied mathematics, Gaussian cumulative probabilities are hard to calculate in any but the univariate case. We offer here an empirical study of the utility of Expectation Propagation (EP) as an approximate integration method for this problem. For rectangular integration regions, the approximation is highly accurate. We also extend the derivations to the more general case of polyhedral integration regions. However, we find that in this polyhedral case, EP's answer, though often accurate, can be almost arbitrarily wrong. These unexpected results elucidate an interesting and non-obvious feature of EP not yet studied in detail, both for the problem of Gaussian probabilities and for EP more generally.

ei pn

Web [BibTex]

Web [BibTex]