Complex Interaction as Emergent Behaviour: Simulating Mid-Air Virtual Keyboard Typing using Reinforcement Learning

Abstract

Accurately modelling user behaviour has the potential to significantly improve the quality of human-computer interaction. Traditionally, these models are carefully hand-crafted to approximate specific aspects of well-documented user behaviour. This limits their availability in virtual and augmented reality where user behaviour is often not yet well understood. Recent efforts have demonstrated that reinforcement learning can approximate human behaviour during simple goal-oriented reaching tasks. We build on these efforts and demonstrate that reinforcement learning can also approximate user behaviour in a complex mid-air interaction task: typing on a virtual keyboard. We present the first reinforcement learning-based user model for mid-air and surface-aligned typing on a virtual keyboard. Our model is shown to replicate high-level human typing behaviour. We demonstrate that this approach may be used to augment or replace human testing during the validation and development of virtual keyboards.

Links

• http://pokristensson.com/pubs/HetzelEtAlTVCG2021.pdf
• doi:10.1109/TVCG.2021.3106494

BibTeX (Download)

@conference{hetzel22,
title = {Complex Interaction as Emergent Behaviour: Simulating Mid-Air Virtual Keyboard Typing using Reinforcement Learning},
author = {Lorenz Hetzel and John Dudley and Anna Maria Feit and Per Ola Kristensson},
url = {http://pokristensson.com/pubs/HetzelEtAlTVCG2021.pdf},
doi = {10.1109/TVCG.2021.3106494},
year  = {2021},
date = {2021-08-27},
urldate = {2021-08-27},
booktitle = {IEEE Transactions on Visualization and Computer Graphics},
publisher = {IEEE},
abstract = {Accurately modelling user behaviour has the potential to significantly improve the quality of human-computer interaction. Traditionally, these models are carefully hand-crafted to approximate specific aspects of well-documented user behaviour. This limits their availability in virtual and augmented reality where user behaviour is often not yet well understood. Recent efforts have demonstrated that reinforcement learning can approximate human behaviour during simple goal-oriented reaching tasks. We build on these efforts and demonstrate that reinforcement learning can also approximate user behaviour in a complex mid-air interaction task: typing on a virtual keyboard. We present the first reinforcement learning-based user model for mid-air and surface-aligned typing on a virtual keyboard. Our model is shown to replicate high-level human typing behaviour. We demonstrate that this approach may be used to augment or replace human testing during the validation and development of virtual keyboards.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}