Po-Jui Huang — accepted AI-conference papers

2023

Curriculum Reinforcement Learning From Avoiding Collisions to Navigating Among Movable Obstacles in Diverse Environments

Hsueh-Cheng Wang, Siao-Cing Huang, Po-Jui Huang, Kuo-Lun Wang, Yi-Chen Teng, Yu-Ting Ko +2

RA-L 2023

Curriculum learning has proven highly effective to speed up training convergence with improved performance in a variety of tasks. Researchers have been studying how a curriculum can be constituted to train reinforcement learning (RL) agents in various application domains. However, discovering curric

Cited by 36 Source ↗Scholar ↗

2021

Cross-Modal Contrastive Learning of Representations for Navigation Using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental Conditions

Jui-Te Huang, Chen-Lung Lu, Po-Kai Chang, Ching-I Huang, Chao-Chun Hsu, Zu Lin Ewe +2

RA-L 2021

Deep reinforcement learning (RL), where the agent learns from mistakes, has been successfully applied to a variety of tasks. With the aim of learning collision-free policies for unmanned vehicles, deep RL has been used for training with various types of data, such as colored images, depth images, an

Cited by 23 Source ↗code ↗Scholar ↗