Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation

TL;DR

This paper introduces a data-efficient method for real-time control of complex physical systems by augmenting physics engines with statistical models, enabling rapid learning with minimal real-world interaction.

Contribution

It presents a novel hybrid approach combining physics simulation and Gaussian process regression for fast, data-efficient control of complex systems in real-time.

Findings

Achieved control within minutes of interaction.

First use of hybrid physics-statistical model for real-time nonlinear control.

Demonstrated effectiveness on a marble maze task.

Abstract

Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success in many complex tasks, these algorithms need a large number of samples to learn meaningful policies. In this paper, we present a task for navigating a marble to the center of a circular maze. While this system is very intuitive and easy for humans to solve, it can be very difficult and inefficient for standard reinforcement learning algorithms to learn meaningful policies. We present a model that learns to move a marble in the complex environment within minutes of interacting with the real system. Learning consists of initializing a physics engine with parameters estimated using data from the real system. The error in the physics engine is then corrected using Gaussian process regression, which is used to model the residual between real observations and physics engine simulations. The physics engine augmented with the residual model is then used to control the marble in the maze environment using a model-predictive feedback over a receding horizon. To the best of our knowledge, this is the first time that a hybrid model consisting of a full physics engine along with a statistical function approximator has been used to control a complex physical system in real-time using nonlinear model-predictive control (NMPC).