Zu Lin Ewe — accepted AI-conference papers

2024

Spatial Graph-Based Localization and Navigation on Scaleless Floorplan

Zu Lin Ewe, Fu-Hao Chang, Yi-Shiang Huang, Li-Chen Fu

RA-L 2024

Effective navigation in unfamiliar environments remains a critical challenge for successful deployment. Current navigation methods, which rely on autonomous or teleoperated exploration and map building, pose technical difficulties for inexperienced end-users. In contrast, humans can effectively navi

Cited by 5 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 ↗