Hoang-Giang Cao
YOU?
Author Swipe
View article: Automatic Foreign Matter Segmentation System for Superabsorbent Polymer Powder: Application of Diffusion Adversarial Representation Learning
Automatic Foreign Matter Segmentation System for Superabsorbent Polymer Powder: Application of Diffusion Adversarial Representation Learning Open
In current industries, sampling inspections of the quality of powders, such as superabsorbent polymers (SAPs) still are conducted via visual inspection. The size of samples and foreign matter are around 500 μm, making them difficult for hu…
View article: Gradient-based Regularization for Action Smoothness in Robotic Control with Reinforcement Learning
Gradient-based Regularization for Action Smoothness in Robotic Control with Reinforcement Learning Open
Deep Reinforcement Learning (DRL) has achieved remarkable success, ranging from complex computer games to real-world applications, showing the potential for intelligent agents capable of learning in dynamic environments. However, its appli…
View article: A SAM-based Solution for Hierarchical Panoptic Segmentation of Crops and Weeds Competition
A SAM-based Solution for Hierarchical Panoptic Segmentation of Crops and Weeds Competition Open
Panoptic segmentation in agriculture is an advanced computer vision technique that provides a comprehensive understanding of field composition. It facilitates various tasks such as crop and weed segmentation, plant panoptic segmentation, a…
View article: Image-based Regularization for Action Smoothness in Autonomous Miniature Racing Car with Deep Reinforcement Learning
Image-based Regularization for Action Smoothness in Autonomous Miniature Racing Car with Deep Reinforcement Learning Open
Deep reinforcement learning has achieved significant results in low-level controlling tasks. However, for some applications like autonomous driving and drone flying, it is difficult to control behavior stably since the agent may suddenly c…
View article: Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors
Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors Open
Picking cluttered general objects is a challenging task due to the complex geometries and various stacking configurations. Many prior works utilize pose estimation for picking, but pose estimation is difficult on cluttered objects. In this…
View article: Learning Sim-to-Real Dense Object Descriptors for Robotic Manipulation
Learning Sim-to-Real Dense Object Descriptors for Robotic Manipulation Open
It is crucial to address the following issues for ubiquitous robotics manipulation applications: (a) vision-based manipulation tasks require the robot to visually learn and understand the object with rich information like dense object desc…
View article: Image-Based Conditioning for Action Policy Smoothness in Autonomous Miniature Car Racing with Reinforcement Learning
Image-Based Conditioning for Action Policy Smoothness in Autonomous Miniature Car Racing with Reinforcement Learning Open
In recent years, deep reinforcement learning has achieved significant results in low-level controlling tasks. However, the problem of control smoothness has less attention. In autonomous driving, unstable control is inevitable since the ve…