Mobile Robotic Rebar Cage Assembly via Imitation Learning

Manipulation remains a key bottleneck in achieving fully autonomous rebar cage assembly. Existing solutions based on rail‑guided systems are expensive, poorly scalable, and limited in capability. This work introduces a framework that leverages a mobile manipulator and uses visual servoing together with imitation learning (IL) to address complex rebar manipulation tasks. Our framework enables, for the first time, autonomous execution of two challenging manipulation tasks: (a) tight-fit rebar slot insertion and (b) rebar tying at complex intersection nodes within cages. Using only low-cost RGB cameras, our approach achieves over 90 % success on both tasks. A highlight is the integration of a segmentation module and a reinsertion strategy that improves tight-fit insertion performance by 41.7 % over the baseline and significantly improves robustness to background changes. Notably, the system requires neither depth sensors nor explicit geometric modeling, and supports rapid deployment in novel environments. This work establishes a foundation for extending autonomy to broader rebar manipulation scenarios