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2012


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Evaluation of Tactile Feedback Methods for Wrist Rotation Guidance

Stanley, A. A., Kuchenbecker, K. J.

IEEE Transactions on Haptics, 5(3):240-251, July 2012 (article)

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[BibTex]

2012


[BibTex]


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Creating realistic virtual textures from contact acceleration data

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

IEEE Transactions on Haptics, 5(2):109-119, April 2012, Cover article (article)

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[BibTex]

[BibTex]


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Simon Game with Data-driven Visuo-audio-haptic Buttons

Castillo, P., Romano, J. M., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Vancouver, Canada, March 2012 (misc)

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[BibTex]

[BibTex]


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Haptic Vibration Feedback for a Teleoperated Ground Vehicle

Healey, S. K., McMahan, W., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Vancouver, Canada, March 2012 (misc)

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[BibTex]

[BibTex]


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A Biofidelic CPR Manikin With Programmable Pneumatic Damping

Stanley, A. A., Healey, S. K., Maltese, M. R., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Vancouver, Canada, March 2012, Finalist for Best Hands-on Demonstration Award (misc)

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[BibTex]

[BibTex]


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StrokeSleeve: Real-Time Vibrotactile Feedback for Motion Guidance

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

Hands-on demonstration presented at IEEE Haptics Symposium, Vancouver, Canada, Vancouver, Canada, March 2012 (misc)

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[BibTex]

[BibTex]


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Pen Tablet Drawing Program with Haptic Textures

Castillo, P., Romano, J. M., Culbertson, H., Mintz, M., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Vancouver, Canada, March 2012 (misc)

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[BibTex]

[BibTex]


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Exploring Presentation Timing through Haptic Reminders

Tam, D., Kuchenbecker, K. J., MacLean, K., McGrenere, J.

Hands-on demonstration presented at IEEE Haptics Symposium, Vancouver, Canada, March 2012 (misc)

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[BibTex]

[BibTex]


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HALO: Haptic Alerts for Low-hanging Obstacles in White Cane Navigation

Wang, Y., Koch, E., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Vancouver, Canada, March 2012, Finalist for Best Hands-on Demonstration Award (misc)

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[BibTex]

[BibTex]


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VerroTeach: Visuo-audio-haptic Training for Dental Caries Detection

Maggio, M. P., Parajon, R., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Vancouver, Canada, March 2012, {B}est Demonstration Award (three-way tie) (misc)

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[BibTex]

[BibTex]


Estimation of MIMO Closed-Loop Poles using Transfer Function Data
Estimation of MIMO Closed-Loop Poles using Transfer Function Data

Vardar, Y.

Eindhoven University of Technology, the Netherlands, 2012 (mastersthesis)

Abstract
For the development of high-tech systems such as lithographic positioning systems, throughput and accuracy are the main requirements. Nowadays, the trend to reach demanded accuracy and throughput levels is designing lightweight and consequently more flexible systems. To control these systems with a more effective and less conservative way, control design should go beyond the traditional rigid control and cope with the flexibilities that limit achievable bandwidth and performance. Therefore, conventional loop shaping methods are not sufficient to reach the performance criterions. Since obtaining an accurate parametric model is very complex and time-consuming for these high-tech systems, using well-developed model-based controller synthesis methods is also not a superior option. To achieve desired performance criterions, one solution can be implemented is reducing the gap between model-based and data-based control synthesis methods. In previous research, a method was developed to define the dynamic behavior of the system without a need for a parametric model. By this method transfer function data (TFD), which provides the information on the whole s-plane can be obtained from frequency response data (FRD) of the system. This innovation was a very important step to use data-based techniques for model-based controller synthesis methods. In this thesis firstly the standard technique to obtain TFD defined in [2] is extended. This standard technique to obtain TFD is not compatible with systems with pure integrators. To extend the methodology also for those systems, two techniques, which are altering the contour and filtering the system, are proposed. Then, the accuracy of TFD is investigated in detail. It is shown that the accuracy of TFD depends on the quality of FRD obtained and the computation techniques used to calculate TFD. Then, a technique which enables to determine the closed-loop poles of a MIMO system using TFD is discussed. The validity of the technique is proven with the help of complex function theory and calculus. Also, the factors that prevent determination of the closed-loop poles are discussed. In addition, it is observed that the accuracy of the closed-loop determination method depends on the quality of obtained TFD and the computation techniques. The proposed theory to obtain TFD and determination of closed-loop poles is validated with experiments conducted to a prototype lightweight system. Also, using experimental frequency response data of NXT-A7 test rig, the success of the proposed methodology is validated also for complex systems. Through these experimental results, it can be concluded that this new technique could be very advantageous in terms of ease of use and accuracy to determine the closed-loop poles of a MIMO lightly damped system.

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[BibTex]

[BibTex]


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Burn-in, bias, and the rationality of anchoring

Lieder, F., Griffiths, T. L., Goodman, N. D.

Advances in Neural Information Processing Systems 25, pages: 2699-2707, 2012 (article)

Abstract
Bayesian inference provides a unifying framework for addressing problems in machine learning, artificial intelligence, and robotics, as well as the problems facing the human mind. Unfortunately, exact Bayesian inference is intractable in all but the simplest models. Therefore minds and machines have to approximate Bayesian inference. Approximate inference algorithms can achieve a wide range of time-accuracy tradeoffs, but what is the optimal tradeoff? We investigate time-accuracy tradeoffs using the Metropolis-Hastings algorithm as a metaphor for the mind's inference algorithm(s). We find that reasonably accurate decisions are possible long before the Markov chain has converged to the posterior distribution, i.e. during the period known as burn-in. Therefore the strategy that is optimal subject to the mind's bounded processing speed and opportunity costs may perform so few iterations that the resulting samples are biased towards the initial value. The resulting cognitive process model provides a rational basis for the anchoring-and-adjustment heuristic. The model's quantitative predictions are tested against published data on anchoring in numerical estimation tasks. Our theoretical and empirical results suggest that the anchoring bias is consistent with approximate Bayesian inference.

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link (url) [BibTex]

link (url) [BibTex]


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Construct Validity of Instrument Vibrations as a Measure of Robotic Surgical Skill

Gomez, E. D., Bark, K., Rivera, C., McMahan, W., Remington, A., Lee, D. I., Williams, N., Murayama, K., Dumon, K., Kuchenbecker, K. J.

Journal of the American College of Surgeons, 215(3):S119-120, Chicago, Illinois, USA, 2012, Oral presentation given by Gomez at the {\em American College of Surgeons (ACS) Clinical Congress} (article)

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[BibTex]

[BibTex]

2007


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Comparing Visual and Haptic Position Feedback

Kuchenbecker, K. J., Gurari, N., Okamura, A. M.

Hands-on demonstration at IEEE World Haptics Conference, Tsukuba, Japan, March 2007 (misc)

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[BibTex]

2007


[BibTex]