Graduate students
PhD student (21.03 ~ )
PhD student at KAIST MS student at KAIST, 2019 Fall – present BS in Computer Science and Engineering, and System Design and Control Engineering from UNIST, 2019
3 notes
2023
We propose a method to refine unreliable plans generated by diffusion-based planners for long-horizon tasks. We introduce a "restoration gap" metric to guide and improve these plans and uses an attribution map regularizer to prevent adversarial guidance. The approach is validated on benchmarks and enhances explainability by highlighting error-prone transitions in the generated plans.
2023
We introduce Variational Curriculum RL (VCRL), a method for unsupervised skill discovery in reinforcement learning by using intrinsic rewards and curriculum learning. The proposed Value Uncertainty Variational Curriculum (VUVC) accelerates skill acquisition and improves state coverage compared to uniform approaches. The approach is validated on complex tasks, showing improved sample efficiency and successful real-world robotic navigation in a zero-shot setup.
2023
We propose a hierarchical framework for robotic navigation that learns a family of diverse low-level policies and a high-level policy to deploy them adaptively. Instead of relying on a fixed reward function, the method learns multiple navigation skills with varied reward functions and selects the most suitable one in real time. The approach is validated in simulation and real-world settings, demonstrating both adaptability and explainability of the agent's behavior.
7 papers