Abstract: Accurate simulation of tendon-driven continuum robots (TDCRs) remains challenging due to their continuous deformation, complex tendon actuation, and strong nonlinearity. One valid approach is learning from real-world data. In this paper, we present a lightweight, low-cost 3D-printed TDCR hardware platform, along with a task-agnostic self-modeling pipeline for learning its kinematic behavior. We employ a point-cloud flow-matching model that learns the robot’s kinematics from randomly sampled kinematic states, capturing the relationship between tendon actuation and the resulting deformation. We evaluate our method via motion prediction experiments, comparing against prior 3D deformable object modeling approaches in both synthetic and real-world settings. The results demonstrate improved accuracy in predicting robot shapes given motor configurations, highlighting the effectiveness of the proposed self-modeling framework for continuum robots.