  Hand-eye calibration is a critical task in robotics, as it directly affects
the efficacy of critical operations such as manipulation and grasping.
Traditional methods for achieving this objective necessitate the careful design
of joint poses and the use of specialized calibration markers, while most
recent learning-based approaches using solely pose regression are limited in
their abilities to diagnose inaccuracies. In this work, we introduce a new
approach to hand-eye calibration called EasyHeC, which is markerless,
white-box, and delivers superior accuracy and robustness. We propose to use two
key technologies: differentiable rendering-based camera pose optimization and
consistency-based joint space exploration, which enables accurate end-to-end
optimization of the calibration process and eliminates the need for the
laborious manual design of robot joint poses. Our evaluation demonstrates
superior performance in synthetic and real-world datasets, enhancing downstream
manipulation tasks by providing precise camera poses for locating and
interacting with objects. The code is available at the project page:
https://ootts.github.io/easyhec.
