DeformGen uses dynamics-based state expansion via localized disturbances and deformation-field warping for trajectory transfer to improve policy learning on deformable manipulation benchmarks.
Tiebot: Learning to knot a tie from visual demonstration through a real-to-sim-to-real approach.arXiv preprint arXiv:2407.03245, 2024
2 Pith papers cite this work. Polarity classification is still indexing.
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A learning framework that predicts pick-and-place affordances for hitch knots from unordered keypoints and images via graph and convolutional autoencoders fused by cross-attention.
citing papers explorer
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DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning
DeformGen uses dynamics-based state expansion via localized disturbances and deformation-field warping for trajectory transfer to improve policy learning on deformable manipulation benchmarks.
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RoboHitch: Learning Visual Affordance from Disordered Keypoints for Hitch Knots Tying
A learning framework that predicts pick-and-place affordances for hitch knots from unordered keypoints and images via graph and convolutional autoencoders fused by cross-attention.