  This work concerns the application of physics-informed neural networks to the
modeling and control of complex robotic systems. Achieving this goal required
extending Physics Informed Neural Networks to handle non-conservative effects.
We propose to combine these learned models with model-based controllers
originally developed with first-principle models in mind. By combining standard
and new techniques, we can achieve precise control performance while proving
theoretical stability bounds. These validations include real-world experiments
of motion prediction with a soft robot and of trajectory tracking with a Franka
Emika manipulator.
