77 citations · 163 across the 24 of their papers we have counts for
6 papers · 1 filter
TODE-Trans: Transparent Object Depth Estimation with Transformer
Kang Chen, Shaochen Wang, Beihao Xia +3
Transparent objects are widely used in industrial automation and daily life. However, robust visual recognition and perception of transparent objects have always been a major chall…
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions
Zhangli Zhou, Shaochen Wang, Ziyang Chen +2
High-resolution representations are important for vision-based robotic grasping problems. Existing works generally encode the input images into low-resolution representations via s…
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion Planning
Hao Zhang, Hao Wang, Zhen Kan
Automaton based approaches have enabled robots to perform various complex tasks. However, most existing automaton based algorithms highly rely on the manually customized representa…
What You See is What You Grasp: User-Friendly Grasping Guided by Near-eye-tracking
Shaochen Wang, Wei Zhang, Zhangli Zhou +5
This work presents a next-generation human-robot interface that can infer and realize the user's manipulation intention via sight only. Specifically, we develop a system that integ…
Temporal Logic Guided Motion Primitives for Complex Manipulation Tasks with User Preferences
Hao Wang, Haoyuan He, Weiwei Shang +1
Dynamic movement primitives (DMPs) are a flexible trajectory learning scheme widely used in motion generation of robotic systems. However, existing DMP-based methods mainly focus o…
When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection
Shaochen Wang, Zhangli Zhou, Zhen Kan
In this paper, we present a transformer-based architecture, namely TF-Grasp, for robotic grasp detection. The developed TF-Grasp framework has two elaborate designs making it well…