6 citations · 7 across the 7 of their papers we have counts for
7 papers
ForeRobo: Unlocking Infinite Simulation Data for 3D Goal-driven Robotic Manipulation
Dexin wang, Faliang Chang, Chunsheng Liu
Efficiently leveraging simulation to acquire advanced manipulation skills is both challenging and highly significant. We introduce \textit{ForeRobo}, a generative robotic agent tha…
Goal State Generation for Robotic Manipulation Based on Linguistically Guided Hybrid Gaussian Diffusion
Yichen Xu, Faliang Chang, Chunsheng Liu +1
In robotic manipulation tasks, achieving a designated target state for the manipulated object is often essential to facilitate motion planning for robotic arms. Specifically, in ta…
Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance
Dexin Wang, Chunsheng Liu, Faliang Chang +1
Decision-making in robotics using denoising diffusion processes has increasingly become a hot research topic, but end-to-end policies perform poorly in tasks with rich contact and…
Multi-Stage Reinforcement Learning for Non-Prehensile Manipulation
Dexin Wang, Faliang Chang, Chunsheng Liu
Manipulating objects without grasping them enables more complex tasks, known as non-prehensile manipulation. Most previous methods only learn one manipulation skill, such as reach…
On-Policy Pixel-Level Grasping Across the Gap Between Simulation and Reality
Dexin Wang, Faliang Chang, Chunsheng Liu +3
Grasp detection in cluttered scenes is a very challenging task for robots. Generating synthetic grasping data is a popular way to train and test grasp methods, as is Dex-net and Gr…
SGDN: Segmentation-Based Grasp Detection Network For Unsymmetrical Three-Finger Gripper
Dexin Wang
In this paper, we present Segmentation-Based Grasp Detection Network (SGDN) to predict a feasible robotic grasping for a unsymmetrical three-finger robotic gripper using RGB images…