This paper presents a novel method for modeling the shape of a continuum robot as a Neural Configuration Euclidean Distance Function (N-CEDF). By learning separate distance fields for each link and combining them through the kinematics chain, the learned N-CEDF provides an accurate and computationally efficient representation of the robot's shape. The key advantage of a distance function representation of a continuum robot is that it enables efficient collision checking for motion planning in dynamic and cluttered environments, even with point-cloud observations. We integrate the N-CEDF into a Model Predictive Path Integral (MPPI) controller to generate safe trajectories for multi-segment continuum robots. The proposed approach is validated for continuum robots with various links in several simulated environments with static and dynamic obstacles.
View on arXiv@article{long2025_2409.13865,
title={ Neural Configuration Distance Function for Continuum Robot Control },
author={ Kehan Long and Hardik Parwana and Georgios Fainekos and Bardh Hoxha and Hideki Okamoto and Nikolay Atanasov },
journal={arXiv preprint arXiv:2409.13865},
year={ 2025 }
}