2 citations · 2 across the 4 of their papers we have counts for
4 papers
Language as Cost: Proactive Hazard Mapping using VLM for Robot Navigation
Mintaek Oh, Chan Kim, Seung-Woo Seo +1
Robots operating in human-centric or hazardous environments must proactively anticipate and mitigate dangers beyond basic obstacle detection. Traditional navigation systems often d…
LaMOuR: Leveraging Language Models for Out-of-Distribution Recovery in Reinforcement Learning
Chan Kim, Seung-Woo Seo, Seong-Woo Kim
Deep Reinforcement Learning (DRL) has demonstrated strong performance in robotic control but remains susceptible to out-of-distribution (OOD) states, often resulting in unreliable…
SeRO: Self-Supervised Reinforcement Learning for Recovery from Out-of-Distribution Situations
Chan Kim, Jaekyung Cho, Christophe Bobda +2
Robotic agents trained using reinforcement learning have the problem of taking unreliable actions in an out-of-distribution (OOD) state. Agents can easily become OOD in real-world…
LoRCoN-LO: Long-term Recurrent Convolutional Network-based LiDAR Odometry
Donghwi Jung, Jae-Kyung Cho, Younghwa Jung +2
We propose a deep learning-based LiDAR odometry estimation method called LoRCoN-LO that utilizes the long-term recurrent convolutional network (LRCN) structure. The LRCN layer is a…