Continual Learning Approaches to Hand-Eye Calibration in Robots

Abstract

This study addresses the problem of hand-eye calibration in robotic systems by developing a continuous learning (CL)-based approach. Traditionally, robots require explicit models to transfer knowledge from camera observations to their hands or base. However, this poses limitations, as the hand-eye calibration parameters are typically valid only for the current camera configuration. To overcome this, we propose a flexible and autonomous hand-eye calibration system that can adapt to changes in camera pose over time. Three CL-based approaches are introduced: the naive CL approach, the reservoir rehearsal approach, and the hybrid approach, combining reservoir sampling with new data evaluation. The naive CL approach suffers from catastrophic forgetting, while the reservoir rehearsal approach mitigates this issue by sampling uniformly from past data. The hybrid approach further enhances performance by incorporating reservoir sampling and assessing new data for novelty. Experimental evaluations conducted in simulated and real-world environments demonstrate that the CL-based approaches, except for the naive one, achieve competitive performance compared to traditional batch learning-based methods. This suggests that treating hand-eye calibration as a time sequence problem enables the extension of the learned space without complete retraining. The adaptability of the CL-based approaches facilitates accommodating changes in camera pose, leading to an improved hand-eye calibration system.