10 citations · 12 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…