9 papers
CUSP: CUSUM-Governed Survival Hazard Alarms at the Perception Onset for Off-Road Navigation
Inuk Kang, Seung-Woo Seo
Off-road navigation exposes a robot to potentially hazardous terrain en route. Although learning-based navigation uses safety supervision to choose which path to drive, it provides…
Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL
Jinwoo Choi, Sang-Hyun Lee, Seung-Woo Seo
Offline goal-conditioned reinforcement learning remains challenging for long-horizon tasks. While hierarchical approaches mitigate this issue by decomposing tasks, most existing me…
Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustment
Jinwoo Choi, Seung-Woo Seo
Reinforcement learning (RL) has made significant progress in various domains, but scaling it to long-horizon tasks with complex decision-making remains challenging. Skill learning…
DIAL: Distribution-Informed Adaptive Learning of Multi-Task Constraints for Safety-Critical Systems
Se-Wook Yoo, Seung-Woo Seo
Safe reinforcement learning has traditionally relied on predefined constraint functions to ensure safety in complex real-world tasks, such as autonomous driving. However, defining…
Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention
Sang-Hyun Lee, Daehyeok Kwon, Seung-Woo Seo
Recent reinforcement learning (RL) algorithms have demonstrated impressive results in simulated driving environments. However, autonomous vehicles trained in simulation often strug…
Results and Lessons Learned from Autonomous Driving Transportation Services in Airfield, Crowded Indoor, and Urban Environments
Doosan Baek, Sanghyun Kim, Seung-Woo Seo +1
Autonomous vehicles have been actively investigated over the past few decades. Several recent works show the potential of autonomous vehicles in urban environments with impressive…
Interpreting Adaptive Gradient Methods by Parameter Scaling for Learning-Rate-Free Optimization
Min-Kook Suh, Seung-Woo Seo
We address the challenge of estimating the learning rate for adaptive gradient methods used in training deep neural networks. While several learning-rate-free approaches have been…
Imagination-Augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments
Sang-Hyun Lee, Yoonjae Jung, Seung-Woo Seo
Hierarchical reinforcement learning (HRL) incorporates temporal abstraction into reinforcement learning (RL) by explicitly taking advantage of hierarchical structure. Modern HRL ty…
Self-Supervised Curriculum Generation for Autonomous Reinforcement Learning without Task-Specific Knowledge
Sang-Hyun Lee, Seung-Woo Seo
A significant bottleneck in applying current reinforcement learning algorithms to real-world scenarios is the need to reset the environment between every episode. This reset proces…