no code implementations • 17 Oct 2023 • Juzhan Xu, Minglun Gong, Hao Zhang, Hui Huang, Ruizhen Hu
We present a novel learning framework to solve the transport-and-packing (TAP) problem in 3D.
no code implementations • 20 Oct 2022 • Zeyu Huang, Juzhan Xu, Sisi Dai, Kai Xu, Hao Zhang, Hui Huang, Ruizhen Hu
Given a few object manipulation demos, NIFT guides the generation of the interaction imitation for a new object instance by matching the Neural Interaction Template (NIT) extracted from the demos in the target Neural Interaction Field (NIF) defined for the new object.
no code implementations • 3 Apr 2022 • Qijin She, Ruizhen Hu, Juzhan Xu, Min Liu, Kai Xu, Hui Huang
To resolve the sample efficiency issue in learning the high-dimensional and complex control of dexterous grasping, we propose an effective representation of grasping state characterizing the spatial interaction between the gripper and the target object.
no code implementations • 3 Mar 2021 • Ruizhen Hu, Bin Chen, Juzhan Xu, Oliver van Kaick, Oliver Deussen, Hui Huang
Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within the entire set.
no code implementations • 3 Sep 2020 • Ruizhen Hu, Juzhan Xu, Bin Chen, Minglun Gong, Hao Zhang, Hui Huang
Using a learning-based approach, a trained network can learn and encode solution patterns to guide the solution of new problem instances instead of executing an expensive online search.