Congratulations to Seunghui, and Daeho 

[Title]
Squeezing the Last Drop of Accuracy: Hand-Eye Calibration via Deep Reinforcement Learning-Guided Pose Tuning

[Journal]

IEEE Robotics and Automation Letters (RA-L) 2025

[Authors]
Seunghui Shin,  Daeho Kim, and Hyoseok Hwang* 

[Summary]
Hand-eye calibration is a fundamental task in robotics, requiring high precision to ensure accurate manipu lation. This is especially crucial for recent markerless methods, which depend on precise pose estimation for effective end5 effector calibration. In this paper, we propose a novel approach that improves calibration performance by adjusting the end effector’s pose to reduce prediction error. Our method utilizes a reward structure derived from trained pose estimation networks, enabling a Soft Actor-Critic-Discrete agent to learn in a simulated environment how to enhance calibration performance through action selection. Our experiments show that calibration results achieved with our method outperform those from initial poses alone in both markerless and marker-based methods. Real-world experiments further validate the efficacy of our approach in actual robotic systems. These results demonstrate that our proposed method effectively enhances the performance of pose estimation-based hand-eye calibration.