Learning 3D shape proprioception for continuum soft robots with multiple magnetic sensors
Thomas Baaij, Marn Klein Holkenborg, Maximilian Stölzle, Daan van der Tuin, Jonatan Naaktgeboren, Robert Babuška, Cosimo Della Santina
Abstract
knowledge from the kinematic model, we obtain an effective yet data-efficient learning strategy. We first demonstrate in simulation the value of this kinematic prior by investigating the proprioception behavior when varying the sensor configuration, which does not require us to re-train the neural network. We validate our approach in experiments involving one soft segment containing a cylindrical magnet and three magnetoresistive sensors. During the experiments, we achieve mean relative errors of 4.5%.
Topics & Concepts
RobotKinematicsA priori and a posterioriMagnetic fieldNonlinear systemComputer scienceProprioceptionArtificial intelligencePhysicsComputer visionClassical mechanicsPsychologyPhilosophyEpistemologyQuantum mechanicsNeuroscienceSoft Robotics and ApplicationsRobot Manipulation and LearningTactile and Sensory Interactions