From Italy to the Netherlands to advance soft-robot modelling

From May to September 2025, Michele Pierallini, researcher at the University of Pisa (Italy), completed his secondment at the Robotics and Mechatronics (RAM) Laboratory of the University of Twente (The Netherlands). His work focused on developing learned and physics-informed models for underactuated soft robotic systems: an approach that combines physical principles with real-world data to better predict complex robot dynamics.

During his stay, Michele collaborated with the RAM team to investigate physically informed neural networks for modelling an elastic cable attached to a drone. He implemented and tested three architectures (feedforward, Lagrangian, and Transformer neural networks), showing that the physics-informed solutions outperform standard approaches in predicting cable deformation during drone motion. Ten experimental trials were carried out to evaluate accuracy and real-time performance, with all models running five times faster than the drone controller and achieving tracking errors below 5 cm.

This work lays the foundation for integrating the learned models into optimal control strategies, with potential applications in environmental sampling and monitoring tasks requiring delicate robot-environment interactions. The secondment produced valuable datasets, neural network models, and preliminary results for a scientific publication currently in preparation.

Stay tuned for further developments as this research advances the next generation of intelligent soft-robot systems!