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2014


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Haptic Robotization of Human Body via Data-Driven Vibrotactile Feedback

Kurihara, Y., Takei, S., Nakai, Y., Hachisu, T., Kuchenbecker, K. J., Kajimoto, H.

Entertainment Computing, 5(4):485-494, December 2014 (article)

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[BibTex]

2014


[BibTex]


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Modeling and Rendering Realistic Textures from Unconstrained Tool-Surface Interactions

Culbertson, H., Unwin, J., Kuchenbecker, K. J.

IEEE Transactions on Haptics, 7(3):381-292, July 2014 (article)

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[BibTex]

[BibTex]

2005


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Contact Location Display for Haptic Perception of Curvature and Object Motion

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

International Journal of Robotics Research, 24(9):691-702, sep 2005 (article)

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[BibTex]

2005


[BibTex]


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Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

Rolinek, M., Swoboda, P., Zietlow, D., Paulus, A., Musil, V., Martius, G.

Arxiv (article)

Abstract
Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Using the presence of heavily optimized combinatorial solvers together with some improvements in architecture design, we advance state-of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, we highlight the conceptual advantages of incorporating solvers into deep learning architectures, such as the possibility of post-processing with a strong multi-graph matching solver or the indifference to changes in the training setting. Finally, we propose two new challenging experimental setups

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Arxiv [BibTex]