← Search

JaeKyung Cho

2 accepted papers

2023

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

IJCAI 2023poster

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 environments because it is almost impossible for them to visit and learn the entire state space during training. Unfortu…

2022

UNICON: Uncertainty-Conditioned Policy for Robust Behavior in Unfamiliar Scenarios

RA-L 2022

Deep reinforcement learning has been used to solve complex tasks in various fields, particularly in robotics control. However, agents trained using deep reinforcement learning have a problem of taking overconfident actions, even when the input state is far from the learned state distribution. This r

Cited by 4SourceScholar