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Learning from multiple demonstrations using trajectory-aware non-rigid registration with applications to deformable object manipulation.

, , , , и . IROS, стр. 5265-5272. IEEE, (2015)

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Dyta: An Intelligent System for Moving Target Detection., , , , , и . ICIAP, стр. 1116-. IEEE Computer Society, (1999)Learning from multiple demonstrations using trajectory-aware non-rigid registration with applications to deformable object manipulation., , , , и . IROS, стр. 5265-5272. IEEE, (2015)Learning force-based manipulation of deformable objects from multiple demonstrations., , , , и . ICRA, стр. 177-184. IEEE, (2015)Evolution of the yeast protein interaction network, , , и . Proceedings of the National Academy of Sciences of the United States of America, 100 (22): 12820--12824 (28.10.2003)Online Learning for Network Traffic Data Classification., , и . ICCE-TW, стр. 1-2. IEEE, (2021)Extending RC4 to Construct Secure Random Number Generators., , , и . ANNSIM, стр. 1-12. IEEE, (2021)New ultrasound image-segmentation algorithm based on an early vision model and discrete snake model., , и . Medical Imaging: Image Processing, том 3338 из SPIE Proceedings, SPIE, (1998)A crosstalk tolerated neural segmentation methodology for Brainbow images., , , и . ISABEL, стр. 190:1-190:5. ACM, (2011)Image Segmentation of Brainbow Images by Using Dirichlet Mixture Models., , и . MLDM Posters, стр. 129-140. ibai Publishing, (2013)Efficient TIS Sensitivity Measurement With Machine Learning Approach and 5G Dataset., , , , , и . CCNC, стр. 1032-1033. IEEE, (2024)