CableSense: MuJoCo Simulation-Guided Neural Networks for Force Estimation in Cable-Driven Manipulators

Abstract

Cable-driven serial manipulator (CDSM) has advantages of lightweight structure, high flexibility, and inherent safety, making it suitable for operations in constrained spaces. However, interaction with the environment is inevitable. To address this limitation, we propose CableSense, a novel force-sensing approach that leverages actuation cable tension information exclusively, thereby eliminating the requirement for additional contact sensors. We first develop a high-fidelity MuJoCo simulation model based on the physical system, reducing the sim-to-real gap through careful calibration of physical and mechanical parameters. Leveraging this simulation model, we generate a comprehensive dataset encompassing diverse external force scenarios. We then implement a multi-task deep learning framework CableSense, for both single-point and multi-point force identification. Experiments demonstrate that CableSense achieves over 98% accuracy in contact location estimation, maintaining a mean absolute direction error of 5.96°.