Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Study on Human-Variability-Respecting Optimal Control Affecting Human Interaction Experience

Sean Kille, Balint Varga, Sören Hohmann

TL;DR

The paper tackles the gap that human motor variability is often ignored in human–machine interaction control. It extends the HVROC framework and uses inverse stochastic optimal control to identify individual human cost and noise, then designs time-variant automation that either preserves or reduces natural variability. A 1D simulated point-mass experimental setup with a haptic interface demonstrates that HVROC can modulate variability and improve task performance in simulation, suggesting feasibility for a real-subject study. The work lays a practical foundation for human-centered control that respects neural and motor variability, with potential to enhance user experience and task quality in real-world HMI deployments.

Abstract

Broad application of human-machine interaction (HMI) demands advanced and human-centered control designs for the machine's automation. Human natural motor action shows stochastic behavior, which has so far not been respected in HMI control designs. Using a previously presented novel human-variability-respecting optimal controller we present a study design which allows the investigation of respecting human natural variability and its effect on human interaction experience. Our approach is tested in simulation based on an identified real human subject and presents a promising approach to be used for a larger subject study.

Study on Human-Variability-Respecting Optimal Control Affecting Human Interaction Experience

TL;DR

The paper tackles the gap that human motor variability is often ignored in human–machine interaction control. It extends the HVROC framework and uses inverse stochastic optimal control to identify individual human cost and noise, then designs time-variant automation that either preserves or reduces natural variability. A 1D simulated point-mass experimental setup with a haptic interface demonstrates that HVROC can modulate variability and improve task performance in simulation, suggesting feasibility for a real-subject study. The work lays a practical foundation for human-centered control that respects neural and motor variability, with potential to enhance user experience and task quality in real-world HMI deployments.

Abstract

Broad application of human-machine interaction (HMI) demands advanced and human-centered control designs for the machine's automation. Human natural motor action shows stochastic behavior, which has so far not been respected in HMI control designs. Using a previously presented novel human-variability-respecting optimal controller we present a study design which allows the investigation of respecting human natural variability and its effect on human interaction experience. Our approach is tested in simulation based on an identified real human subject and presents a promising approach to be used for a larger subject study.
Paper Structure (12 sections, 7 equations, 2 figures)

This paper contains 12 sections, 7 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Materials and Methods
  3. Human-Machine System Model
  4. Human Action
  5. Experimental System
  6. Identification
  7. Automation
  8. Study Procedure
  9. Procedure
  10. Numerical example
  11. Discussion
  12. Conclusion

Figures (2)

  • Figure 1: Experimental setup.
  • Figure 2: From ground-truth data to simulated interaction of mean and variance of the position $p_x$. Human-only measured data is depicted in gray and dashed black and LQS human simulation behavior is depicted in red. The joint behavior of our proposed HVROC with the simulated human is shown in blue; solid and dotted lines represent a high and low desired variability in the task-irrelevant area, respectively.