Tendon-Driven Continuum Robots (TDCRs) pose significant modeling and control challenges due to complex nonlinearities, such as frictional hysteresis and transmission compliance. This paper proposes a differentiable learning framework that integrates high-fidelity dynamics modeling with robust neural control. We develop a GRU-based dynamics model featuring bidirectional multi-channel connectivity and residual prediction to effectively suppress compounding errors during long-horizon auto-regressive prediction. By treating this model as a gradient bridge, an end-to-end neural control policy is optimized through backpropagation, allowing it to implicitly internalize compensation for intricate nonlinearities. Experimental validation on a physical three-section TDCR demonstrates that our framework achieves accurate tracking and superior robustness against unseen payloads, outperforming Jacobian-based methods by eliminating self-excited oscillations.
@article{arxiv.2604.25691,
title = {Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots},
author = {Ziqing Zou and Ke Qiu and Fei Wang and Haojian Lu and Rong Xiong and Yue Wang},
journal= {arXiv preprint arXiv:2604.25691},
year = {2026}
}