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2014


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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]

2014


PDF DOI [BibTex]


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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]


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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]


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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]


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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]


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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]


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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]


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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]


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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]


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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]


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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]


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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]


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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]


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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]


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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]

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]

2007


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Towards compliant humanoids: an experimental assessment of suitable task space position/orientation controllers

Nakanishi, J., Mistry, M., Peters, J., Schaal, S.

In IROS 2007, 2007, pages: 2520-2527, (Editors: Grant, E. , T. C. Henderson), IEEE Service Center, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems, November 2007 (inproceedings)

Abstract
Compliant control will be a prerequisite for humanoid robotics if these robots are supposed to work safely and robustly in human and/or dynamic environments. One view of compliant control is that a robot should control a minimal number of degrees-of-freedom (DOFs) directly, i.e., those relevant DOFs for the task, and keep the remaining DOFs maximally compliant, usually in the null space of the task. This view naturally leads to task space control. However, surprisingly few implementations of task space control can be found in actual humanoid robots. This paper makes a first step towards assessing the usefulness of task space controllers for humanoids by investigating which choices of controllers are available and what inherent control characteristics they have—this treatment will concern position and orientation control, where the latter is based on a quaternion formulation. Empirical evaluations on an anthropomorphic Sarcos master arm illustrate the robustness of the different controllers as well as the eas e of implementing and tuning them. Our extensive empirical results demonstrate that simpler task space controllers, e.g., classical resolved motion rate control or resolved acceleration control can be quite advantageous in face of inevitable modeling errors in model-based control, and that well chosen formulations are easy to implement and quite robust, such that they are useful for humanoids.

ei

PDF Web DOI [BibTex]

2007


PDF Web DOI [BibTex]


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Performance Stabilization and Improvement in Graph-based Semi-supervised Learning with Ensemble Method and Graph Sharpening

Choi, I., Shin, H.

In Korean Data Mining Society Conference, pages: 257-262, Korean Data Mining Society, Seoul, Korea, Korean Data Mining Society Conference, November 2007 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Discriminative Subsequence Mining for Action Classification

Nowozin, S., BakIr, G., Tsuda, K.

In ICCV 2007, pages: 1919-1923, IEEE Computer Society, Los Alamitos, CA, USA, 11th IEEE International Conference on Computer Vision, October 2007 (inproceedings)

Abstract
Recent approaches to action classification in videos have used sparse spatio-temporal words encoding local appearance around interesting movements. Most of these approaches use a histogram representation, discarding the temporal order among features. But this ordering information can contain important information about the action itself, e.g. consider the sport disciplines of hurdle race and long jump, where the global temporal order of motions (running, jumping) is important to discriminate between the two. In this work we propose to use a sequential representation which retains this temporal order. Further, we introduce Discriminative Subsequence Mining to find optimal discriminative subsequence patterns. In combination with the LPBoost classifier, this amounts to simultaneously learning a classification function and performing feature selection in the space of all possible feature sequences. The resulting classifier linearly combines a small number of interpretable decision functions, each checking for the presence of a single discriminative pattern. The classifier is benchmarked on the KTH action classification data set and outperforms the best known results in the literature.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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A Hilbert Space Embedding for Distributions

Smola, A., Gretton, A., Song, L., Schölkopf, B.

In Algorithmic Learning Theory, Lecture Notes in Computer Science 4754 , pages: 13-31, (Editors: M Hutter and RA Servedio and E Takimoto), Springer, Berlin, Germany, 18th International Conference on Algorithmic Learning Theory (ALT), October 2007 (inproceedings)

Abstract
We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in two-sample tests, which are used for determining whether two sets of observations arise from the same distribution, covariate shift correction, local learning, measures of independence, and density estimation.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Cluster Identification in Nearest-Neighbor Graphs

Maier, M., Hein, M., von Luxburg, U.

In ALT 2007, pages: 196-210, (Editors: Hutter, M. , R. A. Servedio, E. Takimoto), Springer, Berlin, Germany, 18th International Conference on Algorithmic Learning Theory, October 2007 (inproceedings)

Abstract
Assume we are given a sample of points from some underlying distribution which contains several distinct clusters. Our goal is to construct a neighborhood graph on the sample points such that clusters are ``identified‘‘: that is, the subgraph induced by points from the same cluster is connected, while subgraphs corresponding to different clusters are not connected to each other. We derive bounds on the probability that cluster identification is successful, and use them to predict ``optimal‘‘ values of k for the mutual and symmetric k-nearest-neighbor graphs. We point out different properties of the mutual and symmetric nearest-neighbor graphs related to the cluster identification problem.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Inducing Metric Violations in Human Similarity Judgements

Laub, J., Macke, J., Müller, K., Wichmann, F.

In Advances in Neural Information Processing Systems 19, pages: 777-784, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
Attempting to model human categorization and similarity judgements is both a very interesting but also an exceedingly difficult challenge. Some of the difficulty arises because of conflicting evidence whether human categorization and similarity judgements should or should not be modelled as to operate on a mental representation that is essentially metric. Intuitively, this has a strong appeal as it would allow (dis)similarity to be represented geometrically as distance in some internal space. Here we show how a single stimulus, carefully constructed in a psychophysical experiment, introduces l2 violations in what used to be an internal similarity space that could be adequately modelled as Euclidean. We term this one influential data point a conflictual judgement. We present an algorithm of how to analyse such data and how to identify the crucial point. Thus there may not be a strict dichotomy between either a metric or a non-metric internal space but rather degrees to which potentially large subsets of stimuli are represented metrically with a small subset causing a global violation of metricity.

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PDF Web [BibTex]

PDF Web [BibTex]


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Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods

Seeger, M.

In Advances in Neural Information Processing Systems 19, pages: 1233-1240, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We propose a highly efficient framework for kernel multi-class models with a large and structured set of classes. Kernel parameters are learned automatically by maximizing the cross-validation log likelihood, and predictive probabilities are estimated. We demonstrate our approach on large scale text classification tasks with hierarchical class structure, achieving state-of-the-art results in an order of magnitude less time than previous work.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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A Local Learning Approach for Clustering

Wu, M., Schölkopf, B.

In Advances in Neural Information Processing Systems 19, pages: 1529-1536, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We present a local learning approach for clustering. The basic idea is that a good clustering result should have the property that the cluster label of each data point can be well predicted based on its neighboring data and their cluster labels, using current supervised learning methods. An optimization problem is formulated such that its solution has the above property. Relaxation and eigen-decomposition are applied to solve this optimization problem. We also briefly investigate the parameter selection issue and provide a simple parameter selection method for the proposed algorithm. Experimental results are provided to validate the effectiveness of the proposed approach.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Branch and Bound for Semi-Supervised Support Vector Machines

Chapelle, O., Sindhwani, V., Keerthi, S.

In Advances in Neural Information Processing Systems 19, pages: 217-224, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
Semi-supervised SVMs (S3VMs) attempt to learn low-density separators by maximizing the margin over labeled and unlabeled examples. The associated optimization problem is non-convex. To examine the full potential of S3VMs modulo local minima problems in current implementations, we apply branch and bound techniques for obtaining exact, globally optimal solutions. Empirical evidence suggests that the globally optimal solution can return excellent generalization performance in situations where other implementations fail completely. While our current implementation is only applicable to small datasets, we discuss variants that can potentially lead to practically useful algorithms.

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PDF Web [BibTex]

PDF Web [BibTex]


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A Kernel Method for the Two-Sample-Problem

Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., Smola, A.

In Advances in Neural Information Processing Systems 19, pages: 513-520, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS). The first test is based on a large deviation bound for the test statistic, while the second is based on the asymptotic distribution of this statistic. The test statistic can be computed in $O(m^2)$ time. We apply our approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where our test performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.

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PDF Web [BibTex]

PDF Web [BibTex]


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An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models

Keerthi, S., Sindhwani, V., Chapelle, O.

In Advances in Neural Information Processing Systems 19, pages: 673-680, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We consider the task of tuning hyperparameters in SVM models based on minimizing a smooth performance validation function, e.g., smoothed k-fold cross-validation error, using non-linear optimization techniques. The key computation in this approach is that of the gradient of the validation function with respect to hyperparameters. We show that for large-scale problems involving a wide choice of kernel-based models and validation functions, this computation can be very efficiently done; often within just a fraction of the training time. Empirical results show that a near-optimal set of hyperparameters can be identified by our approach with very few training rounds and gradient computations.

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PDF Web [BibTex]

PDF Web [BibTex]