6 papers
DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction
Jehun Kang, Jungha Wang, Youngjun Hwang +1
Multi-Task Learning (MTL) in robotics perception systems supports comprehensive 3D spatial scene understanding by integrating semantic segmentation and depth estimation. While Visi…
VLA-R: Vision-Language Action Retrieval toward Open-World End-to-End Autonomous Driving
Hyunki Seong, Seongwoo Moon, Hojin Ahn +2
Exploring open-world situations in an end-to-end manner is a promising yet challenging task due to the need for strong generalization capabilities. In particular, end-to-end autono…
Learning from Demonstration with Hierarchical Policy Abstractions Toward High-Performance and Courteous Autonomous Racing
Chanyoung Chung, Hyunki Seong, David Hyunchul Shim
Fully autonomous racing demands not only high-speed driving but also fair and courteous maneuvers. In this paper, we propose an autonomous racing framework that learns complex raci…
Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity
Hyunki Seong, David Hyunchul Shim
We introduce MoNet, a novel functionally modular network for self-supervised and interpretable end-to-end learning. By leveraging its functional modularity with a latent-guided con…
Skill Q-Network: Learning Adaptive Skill Ensemble for Mapless Navigation in Unknown Environments
Hyunki Seong, David Hyunchul Shim
This paper focuses on the acquisition of mapless navigation skills within unknown environments. We introduce the Skill Q-Network (SQN), a novel reinforcement learning method featur…
Topological Exploration using Segmented Map with Keyframe Contribution in Subterranean Environments
Boseong Kim, Hyunki Seong, D. Hyunchul Shim
Existing exploration algorithms mainly generate frontiers using random sampling or motion primitive methods within a specific sensor range or search space. However, frontiers gener…