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2014


Probabilistic Progress Bars
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.

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website+code pdf DOI [BibTex]

2014


website+code pdf DOI [BibTex]


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Automatic Skill Evaluation for a Needle Passing Task in Robotic Surgery

Leung, S., Kuchenbecker, K. J.

In Proc. IROS Workshop on the Role of Human Sensorimotor Control in Robotic Surgery, Chicago, Illinois, sep 2014, Poster presentation given by Kuchenbecker. Best Poster Award (inproceedings)

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[BibTex]

[BibTex]


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A Data-driven Approach to Remote Tactile Interaction: From a BioTac Sensor to Any Fingertip Cutaneous Device

Pacchierotti, C., Prattichizzo, D., Kuchenbecker, K. J.

In Haptics: Neuroscience, Devices, Modeling, and Applications, Proc. EuroHaptics, Part I, 8618, pages: 418-424, Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, June 2014, Poster presentation given by Pacchierotti in Versailles, France (inproceedings)

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[BibTex]

[BibTex]


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Evaluating the BioTac’s Ability to Detect and Characterize Lumps in Simulated Tissue

Hui, J. C. T., Kuchenbecker, K. J.

In Haptics: Neuroscience, Devices, Modeling, and Applications, Proc. EuroHaptics, Part II, 8619, pages: 295-302, Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, June 2014, Poster presentation given by Hui in Versailles, France (inproceedings)

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[BibTex]

[BibTex]


Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
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.

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

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


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Analyzing Human High-Fives to Create an Effective High-Fiving Robot

Fitter, N. T., Kuchenbecker, K. J.

In Proc. ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages: 156-157, Bielefeld, Germany, March 2014, Poster presentation given by Fitter (inproceedings)

hi

[BibTex]

[BibTex]


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Dynamic Modeling and Control of Voice-Coil Actuators for High-Fidelity Display of Haptic Vibrations

McMahan, W., Kuchenbecker, K. J.

In Proc. IEEE Haptics Symposium, pages: 115-122, Houston, Texas, USA, February 2014, Oral presentation given by Kuchenbecker (inproceedings)

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[BibTex]

[BibTex]


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A Wearable Device for Controlling a Robot Gripper With Fingertip Contact, Pressure, Vibrotactile, and Grip Force Feedback

Pierce, R. M., Fedalei, E. A., Kuchenbecker, K. J.

In Proc. IEEE Haptics Symposium, pages: 19-25, Houston, Texas, USA, February 2014, Oral presentation given by Pierce (inproceedings)

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[BibTex]

[BibTex]


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Methods for Robotic Tool-Mediated Haptic Surface Recognition

Romano, J. M., Kuchenbecker, K. J.

In Proc. IEEE Haptics Symposium, pages: 49-56, Houston, Texas, USA, February 2014, Oral presentation given by Kuchenbecker. Finalist for Best Paper Award (inproceedings)

hi

[BibTex]

[BibTex]


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One Hundred Data-Driven Haptic Texture Models and Open-Source Methods for Rendering on 3D Objects

Culbertson, H., Delgado, J. J. L., Kuchenbecker, K. J.

In Proc. IEEE Haptics Symposium, pages: 319-325, Houston, Texas, USA, February 2014, Poster presentation given by Culbertson. Finalist for Best Poster Award (inproceedings)

hi

[BibTex]

[BibTex]


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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)

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Web link (url) [BibTex]

Web link (url) [BibTex]


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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)

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PDF Web [BibTex]

PDF Web [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)

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DOI [BibTex]

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)

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Web link (url) [BibTex]

Web link (url) [BibTex]


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Algorithm selection by rational metareasoning as a model of human strategy selection

Lieder, F., Plunkett, D., Hamrick, J. B., Russell, S. J., Hay, N. J., Griffiths, T. L.

In Advances in Neural Information Processing Systems 27, 2014 (inproceedings)

Abstract
Selecting the right algorithm is an important problem in computer science, because the algorithm often has to exploit the structure of the input to be efficient. The human mind faces the same challenge. Therefore, solutions to the algorithm selection problem can inspire models of human strategy selection and vice versa. Here, we view the algorithm selection problem as a special case of metareasoning and derive a solution that outperforms existing methods in sorting algorithm selection. We apply our theory to model how people choose between cognitive strategies and test its prediction in a behavioral experiment. We find that people quickly learn to adaptively choose between cognitive strategies. People's choices in our experiment are consistent with our model but inconsistent with previous theories of human strategy selection. Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.

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Project Page [BibTex]

Project Page [BibTex]


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Cutaneous Feedback of Planar Fingertip Deformation and Vibration on a da Vinci Surgical Robot

Pacchierotti, C., Shirsat, P., Koehn, J. K., Prattichizzo, D., Kuchenbecker, K. J.

In Proc. IROS Workshop on the Role of Human Sensorimotor Control in Robotic Surgery, Chicago, Illinois, 2014, Poster presentation given by Koehn (inproceedings)

hi

[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)

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PDF link (url) [BibTex]

PDF 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.

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PDF link (url) DOI [BibTex]

PDF link (url) 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)

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[BibTex]

[BibTex]


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The high availability of extreme events serves resource-rational decision-making

Lieder, F., Hsu, M., Griffiths, T. L.

In Proceedings of the 36th Annual Conference of the Cognitive Science Society, 2014 (inproceedings)

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[BibTex]

[BibTex]


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Layers of Abstraction: (Neuro)computational models of learning local and global statistical regularities

Diaconescu, A., Lieder, F., Mathys, C., Stephan, K. E.

In 20th Annual Meeting of the Organization for Human Brain Mapping, 2014 (inproceedings)

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[BibTex]

[BibTex]

2005


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Perception of Curvature and Object Motion Via Contact Location Feedback

Provancher, W. R., Kuchenbecker, K. J., Niemeyer, G., Cutkosky, M. R.

In Proceedings of the International Symposium on Robotics Research (ISRR), 15, pages: 456-465, Springer Tracts in Advanced Robotics, Springer, Siena, Italy, 2005, Oral presentation given by Provancher in October of 2003 (inproceedings)

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[BibTex]

2005


[BibTex]


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Modeling Induced Master Motion in Force-Reflecting Teleoperation

Kuchenbecker, K. J., Niemeyer, G.

In Proc. IEEE International Conference on Robotics and Automation, pages: 348-353, Barcelona, Spain, April 2005, Oral presentation given by Kuchenbecker (inproceedings)

hi

[BibTex]

[BibTex]


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Event-Based Haptics and Acceleration Matching: Portraying and Assessing the Realism of Contact

Kuchenbecker, K. J., Fiene, J. P., Niemeyer, G.

In Proc. IEEE World Haptics Conference, pages: 381-387, Pisa, Italy, March 2005, Oral presentation given by Kuchenbecker (inproceedings)

hi

[BibTex]

[BibTex]


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Learning to Feel the Physics of a Body

Der, R., Hesse, F., Martius, G.

In Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 , 2, pages: 252-257, Washington, DC, USA, 2005 (inproceedings)

Abstract
Despite the tremendous progress in robotic hardware and in both sensorial and computing efficiencies the performance of contemporary autonomous robots is still far below that of simple animals. This has triggered an intensive search for alternative approaches to the control of robots. The present paper exemplifies a general approach to the self-organization of behavior which has been developed and tested in various examples in recent years. We apply this approach to an underactuated snake like artifact with a complex physical behavior which is not known to the controller. Due to the weak forces available, the controller so to say has to develop a kind of feeling for the body which is seen to emerge from our approach in a natural way with meandering and rotational collective modes being observed in computer simulation experiments.

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[BibTex]

[BibTex]

2004


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Canceling Induced Master Motion in Force-Reflecting Teleoperation

Kuchenbecker, K. J., Niemeyer, G.

In Proc. ASME International Mechanical Engineering Congress and Exposition, Symposium on Advances in Robot Dynamics and Control, 2, paper number 60049, Anaheim, California, USA, November 2004, Oral presentation given by Kuchenbecker. {B}est Student Paper Award (inproceedings)

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[BibTex]

2004


[BibTex]


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Haptic Display of Contact Location

Kuchenbecker, K. J., Provancher, W. R., Niemeyer, G., Cutkosky, M. R.

In Proc. IEEE Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pages: 40-47, Chicago, Illinois, USA, March 2004, Oral presentation given by Kuchenbecker (inproceedings)

hi

[BibTex]

[BibTex]


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THUMP: An Immersive Haptic Console for Surgical Simulation and Training

Niemeyer, G., Kuchenbecker, K. J., Bonneau, R., Mitra, P., Reid, A., Fiene, J., Weldon, G.

In Proc. Medicine Meets Virtual Reality, pages: 272-274, Newport Beach, California, USA, January 2004, Poster presentation given by Niemeyer. {B}est Poster Award (inproceedings)

hi

[BibTex]

[BibTex]