Interpretable Intuitive Physics Model - Robotics Institute Carnegie Mellon University

Interpretable Intuitive Physics Model

Tian Ye
Master's Thesis, Tech. Report, CMU-RI-TR-19-16, Robotics Institute, Carnegie Mellon University, May, 2019

Abstract

Humans have a remarkable ability to use physical commonsense and predict the effect of collisions. But do they understand the underlying factors? Can they predict if the underlying factors have changed? Interestingly, in most cases humans can predict the effects of similar collisions with different conditions such as changes in mass, friction, etc. It is postulated this is primarily because we learn to model physics with meaningful latent variables. This does not imply we can estimate the precise values of these meaningful variables (estimate exact values of mass or friction). Inspired by this observation, we propose an interpretable intuitive physics model where specific dimensions in the bottleneck layers correspond to different physical properties. In order to demonstrate that our system models these underlying physical properties, we train our model on collisions of different shapes (cube, cone, cylinder, spheres etc.) and test on collisions of unseen combinations of shapes. Furthermore, we demonstrate our model generalizes well even when similar scenes are simulated with different underlying properties.

BibTeX

@mastersthesis{Ye-2019-112864,
author = {Tian Ye},
title = {Interpretable Intuitive Physics Model},
year = {2019},
month = {May},
school = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-19-16},
keywords = {Intuitive Physics, Interpretable Models, Physical Properties},
}