most citedSeRO: Self-Supervised Reinforcement Learning for Recovery from Out-of-Distribution Situations

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

cs.RO2025

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…

cs.RO2025

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…

cs.LG2023★ 2 cited

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…