Tendon-Driven Continuum Robots (TDCRs) pose significant control challenges due to their highly nonlinear, path-dependent dynamics and non-Markovian characteristics. Traditional Jacobian-based controllers often struggle with hysteresis-induced oscillations, while conventional learning-based approaches suffer from poor generalization to out-of-distribution trajectories. This paper proposes a reference-augmented offline learning framework for precise 6-DOF tracking control of TDCRs. By leveraging a differentiable RNN-based dynamics surrogate as a gradient bridge, we optimize a control policy through an augmented reference distribution. This multi-scale augmentation scheme incorporates stochastic bias, harmonic perturbations, and random walks, forcing the policy to internalize diverse tracking error recovery mechanisms without additional hardware interaction. Experimental results on a three-section TDCR platform demonstrate that the proposed policy achieves a 50.9\% reduction in average position error compared to non-augmented baselines and significantly outperforms Jacobian-based methods in both precision and stability across various speeds.

翻译：腱驱动连续体机器人(TDCRs)因其高度非线性、路径依赖动力学以及非马尔可夫特性，给控制带来了重大挑战。传统的基于雅可比矩阵的控制器常难以应对迟滞引起的振荡，而基于学习的常规方法则难以泛化到分布外的轨迹。本文提出一种参考增强的离线学习框架，用于实现TDCRs的精确六自由度跟踪控制。通过利用基于可微RNN的动力学代理模型作为梯度桥梁，我们通过增强的参考分布优化控制策略。这种多尺度增强方案融合了随机偏差、谐波扰动和随机游走，迫使策略内化多样化的跟踪误差恢复机制，而无需额外的硬件交互。在三段TDCR平台上的实验结果表明，与未增强的基线方法相比，所提策略的平均位置误差降低了50.9%，且在多种速度下的精度和稳定性均显著优于基于雅可比矩阵的方法。