10 citations · 15 across the 4 of their papers we have counts for
5 papers
HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration
Xingyu Liu, Deepak Pathak, Kris M. Kitani
The ability to learn from human demonstration endows robots with the ability to automate various tasks. However, directly learning from human demonstration is challenging since the…
Tripartite: Tackle Noisy Labels by a More Precise Partition
Xuefeng Liang, Longshan Yao, Xingyu Liu +1
Samples in large-scale datasets may be mislabeled due to various reasons, and Deep Neural Networks can easily over-fit to the noisy label data. To tackle this problem, the key poin…
V-MAO: Generative Modeling for Multi-Arm Manipulation of Articulated Objects
Xingyu Liu, Kris M. Kitani
Manipulating articulated objects requires multiple robot arms in general. It is challenging to enable multiple robot arms to collaboratively complete manipulation tasks on articula…
KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation
Xingyu Liu, Shun Iwase, Kris M. Kitani
We present KDFNet, a novel method for 6D object pose estimation from RGB images. To handle occlusion, many recent works have proposed to localize 2D keypoints through pixel-wise vo…
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering
Shun Iwase, Xingyu Liu, Rawal Khirodkar +2
We present RePOSE, a fast iterative refinement method for 6D object pose estimation. Prior methods perform refinement by feeding zoomed-in input and rendered RGB images into a CNN…