3 citations · 3 across the 8 of their papers we have counts for
9 papers
DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers
Seokbin Yoon, Kyungmin Kim, Taejung Kwon +2
Understanding how many aircraft, and which traffic configurations, can be safely and efficiently managed by air traffic controllers within a given airspace is fundamental to air tr…
The Interplay of Harness Design and Post-Training in LLM Agents
Kyungmin Kim, Youngbin Choi, Seoyeon Lee +3
Tool-integrated LLM agents are often wrapped within a harness: the scaffolding that determines which tools are exposed, how they are described, and what auxiliary information accom…
Verifiable Foundation Models for Robot Safety
Davide Corsi, Kyungmin Kim, Roy Fox
Deploying foundation models for robot control raises a central challenge: the expressive power that enables rich, multimodal perception also makes these models opaque and difficult…
Probabilistic Multi-Agent Aircraft Landing Time Prediction
Kyungmin Kim, Seokbin Yoon, Keumjin Lee
Accurate and reliable aircraft landing time prediction is essential for effective resource allocation in air traffic management. However, the inherent uncertainty of aircraft traje…
Model-Based Reinforcement Learning under Random Observation Delays
Armin Karamzade, Kyungmin Kim, JB Lanier +2
Delays frequently occur in real-world environments, yet standard reinforcement learning (RL) algorithms often assume instantaneous perception of the environment. We study random se…
Adapting World Models with Latent-State Dynamics Residuals
JB Lanier, Kyungmin Kim, Armin Karamzade +5
Simulation-to-reality reinforcement learning (RL) faces the critical challenge of reconciling discrepancies between simulated and real-world dynamics, which can severely degrade ag…
Realizable Continuous-Space Shields for Safe Reinforcement Learning
Kyungmin Kim, Davide Corsi, Andoni Rodriguez +5
While Deep Reinforcement Learning (DRL) has achieved remarkable success across various domains, it remains vulnerable to occasional catastrophic failures without additional safegua…
Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions
Kyungmin Kim, JB Lanier, Pierre Baldi +2
Recent advancements in Model-Based Reinforcement Learning (MBRL) have made it a powerful tool for visual control tasks. Despite improved data efficiency, it remains challenging to…
Reinforcement Learning from Delayed Observations via World Models
Armin Karamzade, Kyungmin Kim, Montek Kalsi +1
In standard reinforcement learning settings, agents typically assume immediate feedback about the effects of their actions after taking them. However, in practice, this assumption…