Efficient Data-Driven Force Sensing for Concentric Tube Continuum Robots using Diffeomorphic-Mapping-Based Transfer Learning

Data-driven techniques show promising results in force estimation for Concentric Tube Continuum Robots, but often require extensive datasets, which are difficult to acquire. This work introduces a mapping-based transfer learning approach to improve the efficiency of data-driven methods for contact force estimation, by proposing a diffeomorphic mapping-based algorithm that reduces data requirements, enabling more practical application of these methods. By transforming data from pre-curved tubes into the feature space of non-curved tubes, our method allows a pre-trained neural network to estimate forces efficiently across various tube configurations, eliminating the need for additional data for changing tube configurations and retraining of the network. Simulation tests show high accuracy for curvatures up to $\kappa = 7\frac{1}{m}$, significantly reducing the need to create large datasets for each new robot configuration.