ABSTRACT This paper presents a novel cable‐climbing mechanism: the Collaborative Climbing Robot Squad (CCRobot‐S), a variant of Reconfigurable Cable‐Driven Parallel Robots (R‐CDPR), specifically designed for the inspection and maintenance of stay cables. The forward kinematics of the CCRobot‐S robotic system, however, is inherently mathematically intractable. This research proposes a novel cascaded strategy with Embodied Artificial Intelligence (EAI) to effectively tackle the forward kinematics problem. In this proposed strategy, a lightweight deep learning‐based model integrated with numerical method optimization supplants traditional methods, providing feedback on the poses of the flying platform to the control loop of the CCRobot‐S robotic system. It provides an approximate solution as initial values through a deep neural network by learning from physical or simulated interactive experiences of CCRobot‐S, and then transfers the suitable initial values with kinematic constraints or physical constraints that are near the real solution to the numerical method. This process achieves a stable and robust solution for the forward kinematics of CCRobot‐S. This article includes the foundational kinematic analysis of CCRobot‐S, the formulation of the CCRobot‐S model, a comprehensive introduction and analysis of the cascaded strategy, including the dataset preparation, the training configuration, the solution inference, and the numerical method optimization. Comprehensive evaluations and experiments were undertaken to examine the proposed strategy. The results reveal and confirm that the deep‐learning neural network implemented in the CCRobot‐S robotic system is effective. Additionally, the proposed cascaded strategy achieves higher prediction accuracy than the standalone neural network approach under the condition of real‐time execution (position error reduced from mm to mm in the X direction, from mm to mm in the Y direction, and from to in the orientation). The cascaded strategy also guarantees convergence in 100 of test cases (50/50) and demonstrates enhanced stability and robustness (1:1 mapping from the joint space to the task space)relative to the conventional Newton‐Raphson algorithm's numerical method. These attributes are crucial and necessary for the CCRobot‐S system to be effectively deployed in real‐world applications.