Document Type
Article
Disciplines
Artificial Intelligence and Robotics | Computer Engineering | Robotics
Abstract
We propose a novel approach to the ’reality gap’ problem, i.e., modifying a robot simulation so that its performance becomes more similar to observed real world phenomena. This problem arises whether the simulation is being used by human designers or in an automated policy development mechanism. We expect that the program/policy is developed using simulation, and subsequently deployed on a real system. We further assume that the program includes a monitor procedure with scalar output to determine when it is achieving its performance objectives. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are used to generate paired roll-outs to identify points of divergence in behavior. These are used to generate statespace kernels that coerce the simulation into behaving more like observed reality. The method was evaluated using ROS/Gazebo for simulation and a heavily modified Traaxas platform in outdoor deployment. The results support not just that the kernel approach can force the simulation to behave more like reality, but that the modification is such that an improved control policy tested in the modified simulation also performs better in the real world.
Publication Title
ArXiv
Article Number
1077
Publication Date
Winter 2-2020
Language
English
Recommended Citation
Lyons, Damian; Finocchiaro, James; Novitzky, Michael; and Korpela, Christopher, "A Monte Carlo Approach to Closing the Reality Gap" (2020). Faculty Publications. 68.
https://research.library.fordham.edu/frcv_facultypubs/68
Version
Pre-publication
Subjects
Robotics, Software Engineering
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
