101 citations · 405 across the 23 of their papers we have counts for
10 papers · 1 filter
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation
Yuzhe Qin, Binghao Huang, Zhao-Heng Yin +2
We propose a sim-to-real framework for dexterous manipulation which can generalize to new objects of the same category in the real world. The key of our framework is to train the m…
LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds
Minghua Liu, Yin Zhou, Charles R. Qi +3
Semantic segmentation of LiDAR point clouds is an important task in autonomous driving. However, training deep models via conventional supervised methods requires large datasets wh…
Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Clouds
Minghua Liu, Xuanlin Li, Zhan Ling +2
We study how choices of input point cloud coordinate frames impact learning of manipulation skills from 3D point clouds. There exist a variety of coordinate frame choices to normal…
Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
Stone Tao, Xiaochen Li, Tongzhou Mu +3
Training long-horizon robotic policies in complex physical environments is essential for many applications, such as robotic manipulation. However, learning a policy that can genera…
A Real2Sim2Real Method for Robust Object Grasping with Neural Surface Reconstruction
Luobin Wang, Runlin Guo, Quan Vuong +3
Recent 3D-based manipulation methods either directly predict the grasp pose using 3D neural networks, or solve the grasp pose using similar objects retrieved from shape databases.…
Multi-skill Mobile Manipulation for Object Rearrangement
Jiayuan Gu, Devendra Singh Chaplot, Hao Su +1
We study a modular approach to tackle long-horizon mobile manipulation tasks for object rearrangement, which decomposes a full task into a sequence of subtasks. To tackle the entir…