Learning-based control for tendon-driven continuum robotic arms

doi:10.3389/frobt.2025.1488869

Learning-based control for tendon-driven continuum robotic arms

2025-08-08

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DOI：10.3389/frobt.2025.1488869

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Tendon-Driven Continuum Robots are widely recognized for their flexibility and adaptability in constrained environments; making them invaluable for most applications; such as medical surgery; industrial tasks; and so on. However; the inherent uncertainties and highly nonlinear dynamics of these manipulators pose significant challenges for classical model-based controllers. Addressing these challenges necessitates the development of advanced control strategies capable of adapting to diverse operational scenarios. This paper presents a centralized position control strategy using Deep Reinforcement Learning; with a particular focus on the Sim-to-Real transfer of control policies. The proposed method employs a customized Modified Transpose Jacobian control strategy for continuum arms; where its parameters are optimally tuned using the Deep Deterministic Policy Gradient algorithm. By integrating an optimal adaptive gain-tuning regulation; the research aims to develop a model-free controller that achieves superior performance compared to ideal model-based strategies. Both simulations and real-world experiments demonstrate that the proposed controller significantly enhances the trajectory-tracking performance of continuum manipulators. The proposed controller achieves robustness across various initial conditions and trajectories; making it a promising candidate for general-purpose applications.

Keywords:

Tendon-Driven Continuum Robots

Deep Reinforcement Learning

Sim-to-Real Transfer

Model-Free Control

Trajectory Tracking

Learning-based control for tendon-driven continuum robotic arms

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