7 citations · 7 across the 4 of their papers we have counts for
7 papers
Gradient-based Regularization for Action Smoothness in Robotic Control with Reinforcement Learning
I Lee, Hoang-Giang Cao, Cong-Tinh Dao +2
Deep Reinforcement Learning (DRL) has achieved remarkable success, ranging from complex computer games to real-world applications, showing the potential for intelligent agents capa…
Multi-Agent Training for Pommerman: Curriculum Learning and Population-based Self-Play Approach
Nhat-Minh Huynh, Hoang-Giang Cao, I-Chen Wu
Pommerman is a multi-agent environment that has received considerable attention from researchers in recent years. This environment is an ideal benchmark for multi-agent training, p…
A SAM-based Solution for Hierarchical Panoptic Segmentation of Crops and Weeds Competition
Khoa Dang Nguyen, Thanh-Hai Phung, Hoang-Giang Cao
Panoptic segmentation in agriculture is an advanced computer vision technique that provides a comprehensive understanding of field composition. It facilitates various tasks such as…
Image-based Regularization for Action Smoothness in Autonomous Miniature Racing Car with Deep Reinforcement Learning
Hoang-Giang Cao, I Lee, Bo-Jiun Hsu +4
Deep reinforcement learning has achieved significant results in low-level controlling tasks. However, for some applications like autonomous driving and drone flying, it is difficul…
Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors
Hoang-Giang Cao, Weihao Zeng, I-Chen Wu
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…
Learning Sim-to-Real Dense Object Descriptors for Robotic Manipulation
Hoang-Giang Cao, Weihao Zeng, I-Chen Wu
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 underst…