Document Type
Article
Keywords
Mobile robots, Sim2real, simulation, learning
Disciplines
Computer Engineering | Robotics
Abstract
Robot software developed in simulation often does not be- have as expected when deployed because the simulation does not sufficiently represent reality - this is sometimes called the `reality gap' problem. We propose a novel algorithm to address the reality gap by injecting real-world experience into the simulation. It is assumed that the robot program (control policy) is developed using simulation, but subsequently deployed on a real system, and that the program includes a performance objective monitor procedure with scalar output. 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 simulation and real behavior. From these, state-space kernels are generated that, when integrated with the original simulation, coerce the simulation into behaving more like observed reality. Performance results are presented for a long-term deployment of an autonomous delivery vehicle example.
Publication Title
INTERNATIONAL CONFERENCE ON INTELLIGENT AUTONOMOUS SYSTEMS
Volume
17
Article Number
1082
Publication Date
Summer 2022
Language
English
Recommended Citation
Lyons, Damian M.; Finocchiaro, James; Novitzky, Misha; and Korpela, Chris, "A Monte Carlo Framework for Incremental Improvement of Simulation Fidelity" (2022). Faculty Publications. 73.
https://research.library.fordham.edu/frcv_facultypubs/73
Version
Published
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
