Structured Mechanical Models for Robot Learning and Control
Model-based methods are the dominant paradigm for controlling robotic systems, though their efficacy depends heavily on the accuracy of the model used. Deep neural networks have been used to learn models of robot dynamics from data due to their ability to express arbitrary phenomena, but suffer from data-inefficiency and the difficulty to incorporate prior knowledge.
We introduce Structured Mechanical Models, a flexible model class for mechanical systems that are data-efficient, easily amenable to prior knowledge, and easily usable with model-based control techniques. We demonstrate the benefits of using Structured Mechanical Models in lieu of black-box neural networks when modeling robot dynamics. We find that they generalize better from limited data and yield more reliable model-based controllers on a variety of simulated robotic domains.
Accepted at L4DC-2020.
Discussion at OpenReview.