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2010


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Realistic Haptic Contacts and Textures for Tablet Computing

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

Hands-on demonstration presented at the Stanford Medical Innovation Conference on Medical Robotics, Stanford, California, April 2010 (misc)

hi

[BibTex]

2010


[BibTex]


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High-Frequency Tactile Feedback for the da Vinci Surgical System

Standish, D., Gewirtz, J., McMahan, W., Martin, P., Kuchenbecker, K. J.

Hands-on demonstration presented at the Stanford Medical Innovation Conference on Medical Robotics, April 2010 (misc)

hi

[BibTex]

[BibTex]


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Machine-Learning Methods for Decoding Intentional Brain States

Hill, NJ.

Symposium "Non-Invasive Brain Computer Interfaces: Current Developments and Applications" (BIOMAG), March 2010 (talk)

Abstract
Brain-computer interfaces (BCI) work by making the user perform a specific mental task, such as imagining moving body parts or performing some other covert mental activity, or attending to a particular stimulus out of an array of options, in order to encode their intention into a measurable brain signal. Signal-processing and machine-learning techniques are then used to decode the measured signal to identify the encoded mental state and hence extract the user‘s initial intention. The high-noise high-dimensional nature of brain-signals make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since “it doesn‘t matter what classifier you use once your features are extracted.” Using examples from our own MEG and EEG experiments, I‘ll demonstrate how machine-learning principles can be applied in order to improve BCI performance, if they are formulated in a domain-specific way. The result is a type of data-driven analysis that is more than “just” classification, and can be used to find better feature extractors.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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PAC-Bayesian Analysis in Unsupervised Learning

Seldin, Y.

Foundations and New Trends of PAC Bayesian Learning Workshop, March 2010 (talk)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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High-Frequency Tactile Feedback for the da Vinci Surgical System

Standish, D., Gewirtz, J., McMahan, W., Martin, P., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, March 2010 (misc)

hi

[BibTex]

[BibTex]


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The Haptic Board

Jiang, Z., Bhoite, M., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, USA, March 2010 (misc)

hi

[BibTex]

[BibTex]


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Tactile Gaming Vest (TGV)

Palan, S., Wang, R., Naukam, N., Li, E., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, March 2010 (misc)

hi

[BibTex]

[BibTex]


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Realistic Haptic Contacts and Textures for Tablet Computing

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

Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, March 2010, {B}est Teaser Award (misc)

hi

[BibTex]

[BibTex]


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GPU-Based Haptic Rendering of 3D Smoke

Yang, M., Lu, J., Safonova, A., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, March 2010 (misc)

hi

[BibTex]

[BibTex]


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Learning Motor Primitives for Robotics

Kober, J., Peters, J.

EVENT Lab: Reinforcement Learning in Robotics and Virtual Reality, January 2010 (talk)

Abstract
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), appropriate learning algorithms for a concerted approach of both imitation and reinforcement learning are presented. Using these algorithms new motor skills, i.e., Ball-in-a-Cup, Ball-Paddling and Dart-Throwing, are learned.

ei

[BibTex]

[BibTex]


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\textscLpzRobots: A free and powerful robot simulator

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

\urlhttp://robot.informatik.uni-leipzig.de/software, 2010 (misc)

al

[BibTex]

[BibTex]


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Playful Machines: Tutorial

Der, R., Martius, G.

\urlhttp://robot.informatik.uni-leipzig.de/tutorial?lang=en, 2010 (misc)

al

[BibTex]

[BibTex]