262 citations · 881 across the 33 of their papers we have counts for
7 papers · 1 filter
iCaps: Iterative Category-level Object Pose and Shape Estimation
Xinke Deng, Junyi Geng, Timothy Bretl +2
This paper proposes a category-level 6D object pose and shape estimation approach iCaps, which allows tracking 6D poses of unseen objects in a category and estimating their 3D shap…
TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information
Suraj Kothawade, Saikat Ghosh, Sumit Shekhar +2
Deep neural networks based object detectors have shown great success in a variety of domains like autonomous vehicles, biomedical imaging, etc. It is known that their success depen…
Hierarchical Policies for Cluttered-Scene Grasping with Latent Plans
Lirui Wang, Xiangyun Meng, Yu Xiang +1
6D grasping in cluttered scenes is a longstanding problem in robotic manipulation. Open-loop manipulation pipelines may fail due to inaccurate state estimation, while most end-to-e…
RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks
Christopher Xie, Arsalan Mousavian, Yu Xiang +1
Segmenting unseen object instances in cluttered environments is an important capability that robots need when functioning in unstructured environments. While previous methods have…
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
Yu-Wei Chao, Wei Yang, Yu Xiang +9
We introduce DexYCB, a new dataset for capturing hand grasping of objects. We first compare DexYCB with a related one through cross-dataset evaluation. We then present a thorough b…
RGB-D Local Implicit Function for Depth Completion of Transparent Objects
Luyang Zhu, Arsalan Mousavian, Yu Xiang +4
Majority of the perception methods in robotics require depth information provided by RGB-D cameras. However, standard 3D sensors fail to capture depth of transparent objects due to…
Learning Composable Behavior Embeddings for Long-horizon Visual Navigation
Xiangyun Meng, Yu Xiang, Dieter Fox
Learning high-level navigation behaviors has important implications: it enables robots to build compact visual memory for repeating demonstrations and to build sparse topological m…