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

, , , , and . IROS, page 5265-5272. IEEE, (2015)

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Learning from multiple demonstrations using trajectory-aware non-rigid registration with applications to deformable object manipulation., , , , and . IROS, page 5265-5272. IEEE, (2015)Dyta: An Intelligent System for Moving Target Detection., , , , , and . ICIAP, page 1116-. IEEE Computer Society, (1999)Learning force-based manipulation of deformable objects from multiple demonstrations., , , , and . ICRA, page 177-184. IEEE, (2015)Evolution of the yeast protein interaction network, , , and . Proceedings of the National Academy of Sciences of the United States of America, 100 (22): 12820--12824 (Oct 28, 2003)Online Learning for Network Traffic Data Classification., , and . ICCE-TW, page 1-2. IEEE, (2021)Extending RC4 to Construct Secure Random Number Generators., , , and . ANNSIM, page 1-12. IEEE, (2021)Image Segmentation of Brainbow Images by Using Dirichlet Mixture Models., , and . MLDM Posters, page 129-140. ibai Publishing, (2013)Efficient TIS Sensitivity Measurement With Machine Learning Approach and 5G Dataset., , , , , and . CCNC, page 1032-1033. IEEE, (2024)New ultrasound image-segmentation algorithm based on an early vision model and discrete snake model., , and . Medical Imaging: Image Processing, volume 3338 of SPIE Proceedings, SPIE, (1998)A crosstalk tolerated neural segmentation methodology for Brainbow images., , , and . ISABEL, page 190:1-190:5. ACM, (2011)