Byungjin Ko

Learning to Grasp by Integrating Human Preferences and Success Feedback

End-to-end robotic grasping increasingly relies on reinforcement learning to enable safe and precise execution, yet defining a reward that consistently drives such behavior remains a central challenge. Human-engineered rewards have been widely explored, but they are prone to reward hacking, depend h…