Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots

TL;DR

This paper introduces a differentiable learning framework combining high-fidelity dynamics modeling with neural control for tendon-driven continuum robots, enhancing accuracy and robustness in complex nonlinear environments.

Contribution

It develops a GRU-based dynamics model with residual prediction and integrates it into an end-to-end neural control policy for improved performance.

Findings

Achieves accurate tracking on a physical three-section TDCR.

Demonstrates robustness against unseen payloads.

Outperforms Jacobian-based methods by eliminating oscillations.

Abstract

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.