14 citations · 16 across the 7 of their papers we have counts for
7 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…
E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models
Chan Kim, Keonwoo Kim, Mintaek Oh +10
Large language models (LLMs) have shown significant potential in guiding embodied agents to execute language instructions across a range of tasks, including robotic manipulation an…
MuNES: Multifloor Navigation Including Elevators and Stairs
Donghwi Jung, Chan Kim, Jae-Kyung Cho +1
We propose a scheme called MuNES for single mapping and trajectory planning including elevators and stairs. Optimized multifloor trajectories are important for optimal interfloor m…
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…
Reinforcement Learning of Display Transfer Robots in Glass Flow Control Systems: A Physical Simulation-Based Approach
Hwajong Lee, Chan Kim, Seong-Woo Kim
A flow control system is a critical concept for increasing the production capacity of manufacturing systems. To solve the scheduling optimization problem related to the flow contro…
GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Driving
Se-Wook Yoo, Chan Kim, Jin-Woo Choi +2
Applying reinforcement learning to autonomous driving entails particular challenges, primarily due to dynamically changing traffic flows. To address such challenges, it is necessar…