Obtaining dynamic models of continuum soft robots is central to the analysis and control of soft robots, and researchers have devoted much attention to the challenge of proposing both data-driven and first-principle solutions. Both avenues have, however, shown their limitations; the former lacks structure and performs poorly outside training data, while the latter requires significant simplifications and extensive expert knowledge to be used in practice. This paper introduces a streamlined method for learning low-dimensional, physicsbased models that are both accurate and easy to interpret. We start with an algorithm that uses image data (i.e., shape evolutions) to determine the minimal necessary segments for describing a soft robot's movement. Following this, we apply a dynamic regression and strain sparsification algorithm to identify relevant strains and define the model's dynamics. We validate our approach through simulations with various planar soft manipulators, comparing its performance against other learning strategies, showing that our models are both computationally efficient and 25x more accurate on out-of-training distribution inputs. Finally, we demonstrate that thanks to the capability of the method of generating physically compatible models, the learned models can be straightforwardly combined with model-based control policies.

We introduce the concept of collaborative awareness as a means to enhance interoperability, resilience and self regulation in synthetic agent collectives. We discuss the theoretical, computational and engineering framework of collaborative awareness built by the EU project EMERGE, and its application to distributed robotic systems.

This work concerns the application of physics-informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics-informed neural networks to handle nonconservative effects. These learned models are proposed to combine with model-based controllers originally developed with first-principle models in mind. By combining standard and new techniques, precise control performance can be achieved while proving theoretical stability bounds. These validations include real-world experiments of motion prediction with a soft robot and trajectory tracking with a Franka Emika Panda manipulator.

This study explores a method for the dynamic modeling of soft robots, focusing on enhancing the deep learning-based Lagrangian modeling approach through the attention mechanism, which enriches the training process by allocating focused attention and analytical weighting to critical state features, thereby increasing the model's sensitivity to changes in the robot's state. We compared our method through simulation, demonstrating that the model is effective in long-term prediction and noise rejection.