101 citations · 307 across the 11 of their papers we have counts for
7 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…
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…
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…
Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics
Sizhe Li, Zhiao Huang, Tao Du +3
Differentiable physics has recently been shown as a powerful tool for solving soft-body manipulation tasks. However, the differentiable physics solver often gets stuck when the ini…
Single RGB-D Camera Teleoperation for General Robotic Manipulation
Quan Vuong, Yuzhe Qin, Runlin Guo +3
We propose a teleoperation system that uses a single RGB-D camera as the human motion capture device. Our system can perform general manipulation tasks such as cloth folding, hamme…
OCRTOC: A Cloud-Based Competition and Benchmark for Robotic Grasping and Manipulation
Ziyuan Liu, Wei Liu, Yuzhe Qin +8
In this paper, we propose a cloud-based benchmark for robotic grasping and manipulation, called the OCRTOC benchmark. The benchmark focuses on the object rearrangement problem, spe…