Synthesizing machine learning-based controllers for underactuated robotic manipulators

A computer-implemented system for generating a feedback controller input to control an underactuated robotic manipulator, the system comprising:

• a processor;
• and
• memory having modules stored thereon with instructions to be executed by the processor, the modules comprising:
• a machine learning based model comprising a plurality of neural network modules, each module configured to approximate one of a plurality of functions related to an underactuated controller for a robotic manipulator, wherein the plurality of functions includes a desired mass, a desired potential energy, and an interconnection matrix;
• wherein parameters of each function are learned during training of the model using a loss function that satisfies one or more conditions including structure preservation, integrability and equilibrium assignment;
• wherein the trained model generates a desired mass matrix and a desired potential energy matrix to enable synthesis of a feedback controller input.