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2015


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Reducing Student Anonymity and Increasing Engagement

Kuchenbecker, K. J.

University of Pennsylvania Almanac, 62(18):8, November 2015 (article)

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[BibTex]

2015


[BibTex]


Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results
Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results

Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.

Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)

Abstract
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Preliminary results of a low-dimensional tuning problem highlight the method’s potential for automatic controller tuning on robotic platforms.

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PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


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Surgeons and Non-Surgeons Prefer Haptic Feedback of Instrument Vibrations During Robotic Surgery

Koehn, J. K., Kuchenbecker, K. J.

Surgical Endoscopy, 29(10):2970-2983, October 2015 (article)

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[BibTex]

[BibTex]


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Displaying Sensed Tactile Cues with a Fingertip Haptic Device

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

IEEE Transactions on Haptics, 8(4):384-396, October 2015 (article)

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[BibTex]

[BibTex]


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A thin film active-lens with translational control for dynamically programmable optical zoom

Yun, S., Park, S., Park, B., Nam, S., Park, S. K., Kyung, K.

Applied Physics Letters, 107(8):081907, AIP Publishing, August 2015 (article)

Abstract
We demonstrate a thin film active-lens for rapidly and dynamically controllable optical zoom. The active-lens is composed of a convex hemispherical polydimethylsiloxane (PDMS) lens structure working as an aperture and a dielectric elastomer (DE) membrane actuator, which is a combination of a thin DE layer made with PDMS and a compliant electrode pattern using silver-nanowires. The active-lens is capable of dynamically changing focal point of the soft aperture as high as 18.4% through its translational movement in vertical direction responding to electrically induced bulged-up deformation of the DE membrane actuator. Under operation with various sinusoidal voltage signals, the movement responses are fairly consistent with those estimated from numerical simulation. The responses are not only fast, fairly reversible, and highly durable during continuous cyclic operations, but also large enough to impart dynamic focus tunability for optical zoom in microscopic imaging devices with a light-weight and ultra-slim configuration.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Toward a large-scale visuo-haptic dataset for robotic learning

Burka, A., Hu, S., Krishnan, S., Kuchenbecker, K. J., Hendricks, L. A., Gao, Y., Darrell, T.

In Proc. CVPR Workshop on the Future of Datasets in Vision, 2015 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Detecting Lumps in Simulated Tissue via Palpation with a BioTac

Hui, J., Block, A., Kuchenbecker, K. J.

In Proc. IEEE World Haptics Conference, 2015, Work-in-progress paper. Poster presentation given by Hui (inproceedings)

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[BibTex]

[BibTex]


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Analysis of the Instrument Vibrations and Contact Forces Caused by an Expert Robotic Surgeon Doing FRS Tasks

Brown, J. D., O’Brien, C., Miyasaka, K., Dumon, K. R., Kuchenbecker, K. J.

In Proc. Hamlyn Symposium on Medical Robotics, pages: 75-76, London, England, June 2015, Poster presentation given by Brown (inproceedings)

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[BibTex]

[BibTex]


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Should Haptic Texture Vibrations Respond to User Force and Speed?

Culbertson, H., Kuchenbecker, K. J.

In IEEE World Haptics Conference, pages: 106 - 112, Evanston, Illinois, USA, June 2015, Oral presentation given by Culbertson (inproceedings)

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[BibTex]

[BibTex]


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Enabling the Baxter Robot to Play Hand-Clapping Games

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

In Proc. IEEE World Haptics Conference, June 2015, Work-in-progress paper. Poster presentation given by Fitter (inproceedings)

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[BibTex]

[BibTex]


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Data-Driven Motion Mappings Improve Transparency in Teleoperation

Khurshid, R. P., Kuchenbecker, K. J.

Presence: Teleoperators and Virtual Environments, 24(2):132-154, May 2015 (article)

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[BibTex]

[BibTex]


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Using IMU Data to Teach a Robot Hand-Clapping Games

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

In Proc. IEEE Haptics Symposium, pages: 353-355, April 2015, Work-in-progress paper. Poster presentation given by Fitter (inproceedings)

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[BibTex]

[BibTex]


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Haptic Feedback in Transoral Robotic Surgery: A Feasibility Study

Bur, A. M., Gomez, E. D., Rassekh, C. H., Newman, J. G., Weinstein, G. S., Kuchenbecker, K. J.

In Proc. Annual Meeting of the Triological Society at COSM, April 2015, Poster presentation given by Bur (inproceedings)

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[BibTex]

[BibTex]


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Haptic Textures for Online Shopping

Culbertson, H., Kuchenbecker, K. J.

Interactive demonstrations in The Retail Collective exhibit, presented at the Dx3 Conference in Toronto, Canada, March 2015 (misc)

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[BibTex]

[BibTex]


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Design and Validation of a Practical Simulator for Transoral Robotic Surgery

Bur, A. M., Gomez, E. D., Chalian, A. A., Newman, J. G., Weinstein, G. S., Kuchenbecker, K. J.

In Proc. Society for Robotic Surgery Annual Meeting: Transoral Program, (T8), February 2015, Oral presentation given by Bur (inproceedings)

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[BibTex]

[BibTex]


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Robotic Learning of Haptic Adjectives Through Physical Interaction

Chu, V., McMahon, I., Riano, L., McDonald, C. G., He, Q., Perez-Tejada, J. M., Arrigo, M., Darrell, T., Kuchenbecker, K. J.

Robotics and Autonomous Systems, 63(3):279-292, 2015, Vivian Chu, Ian MacMahon, and Lorenzo Riano contributed equally to this publication. Corrigendum published in June 2016 (article)

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[BibTex]

[BibTex]


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Effects of Vibrotactile Feedback on Human Motor Learning of Arbitrary Arm Motions

Bark, K., Hyman, E., Tan, F., Cha, E., Jax, S. A., Buxbaum, L. J., Kuchenbecker, K. J.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(1):51-63, January 2015 (article)

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[BibTex]

[BibTex]


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Inference of Cause and Effect with Unsupervised Inverse Regression

Sgouritsa, E., Janzing, D., Hennig, P., Schölkopf, B.

In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38, pages: 847-855, JMLR Workshop and Conference Proceedings, (Editors: Lebanon, G. and Vishwanathan, S.V.N.), JMLR.org, AISTATS, 2015 (inproceedings)

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Web PDF [BibTex]

Web PDF [BibTex]


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Probabilistic Interpretation of Linear Solvers

Hennig, P.

SIAM Journal on Optimization, 25(1):234-260, 2015 (article)

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Web PDF link (url) DOI [BibTex]

Web PDF link (url) DOI [BibTex]


Probabilistic Line Searches for Stochastic Optimization
Probabilistic Line Searches for Stochastic Optimization

Mahsereci, M., Hennig, P.

In Advances in Neural Information Processing Systems 28, pages: 181-189, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (inproceedings)

Abstract
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent. [You can find the matlab research code under `attachments' below. The zip-file contains a minimal working example. The docstring in probLineSearch.m contains additional information. A more polished implementation in C++ will be published here at a later point. For comments and questions about the code please write to mmahsereci@tue.mpg.de.]

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Matlab research code link (url) [BibTex]

Matlab research code link (url) [BibTex]


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A Random Riemannian Metric for Probabilistic Shortest-Path Tractography

Hauberg, S., Schober, M., Liptrot, M., Hennig, P., Feragen, A.

In 18th International Conference on Medical Image Computing and Computer Assisted Intervention, 9349, pages: 597-604, Lecture Notes in Computer Science, MICCAI, 2015 (inproceedings)

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PDF DOI [BibTex]

PDF DOI [BibTex]


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Probabilistic numerics and uncertainty in computations

Hennig, P., Osborne, M. A., Girolami, M.

Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 471(2179), 2015 (article)

Abstract
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data have led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimizers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations.

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PDF DOI [BibTex]

PDF DOI [BibTex]