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RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation.
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2026-02-26 03:20:36
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RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation Chongkai Gao 1 , Zhengrong Xue 2 , Shuying Deng 2 , Tianhai Liang 2 , Siqi Yang 2 , Lin Shao 1 , Huazhe Xu 234 1 National Unversity of Singapore, 2 Tsinghua Unversity 3 Shanghai AI Lab 4 Shanghai Qizhi Institute Paper Code RiEMann generalizes to new SE(3) poses, new object instances, and resists visual distracting, and has real-time following ability, only with 5 to 10 demonstrations for object manipulation tasks. Abstract We present RiEMann, an end-to-end near Real-time SE(3)-Equivariant Robot Manipulation imitation learning framework from scene point cloud input. Compared to previous methods that rely on descriptor field matching, RiEMann directly predicts the target poses of objects for manipulation witho...
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