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Real World Bayesian Optimization Using Robots to Clean Liquid Spills

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Voysey, I 
Thuruthel, TG 
Gilday, K 

Abstract

Developing robots that can contribute to cleaning could have a significant impact on the lives of many. Cleaning wet liquid spills is a particularly challenging task for a robotic system, and has several high impact applications. This is a hard task to physically model due to the complex interactions between cleaning materials and the surface. As such, to the authors' knowledge there has been no prior work in this area. A new method for finding optimal control parameters for the cleaning of liquid spills is required by developing a robotic system which iteratively learns to clean through physical experimentation. The robot creates a liquid spill, cleans and assesses performance and uses Bayesian optimization to find the optimal control parameters for a given size of liquid spill. The automation process enabled the experiment to be repeated more than 400 times over 20 hours to find the optimal wiping control parameters for many different conditions. We then show that these solutions can be extrapolated for different spill conditions. The optimized control parameters showed reliable and accurate performances, which in some cases, outperformed humans at the same task.

Description

Keywords

46 Information and Computing Sciences, 4608 Human-Centred Computing, 4602 Artificial Intelligence

Journal Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Conference Name

Towards Autonomous Robotic Systems - 21st Annual Conference, TAROS 2020, Nottingham, UK, Proceedings - TAROS 2020

Journal ISSN

0302-9743
1611-3349

Volume Title

12228 LNAI

Publisher

Springer International Publishing

Rights

All rights reserved
Sponsorship
This work was supported by BEKO PLC and Symphony Kitchens. We are especially thankful for the valuable inputs from Dr Graham Anderson and Dr Natasha Conway.