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Preference-Based Human-in-the-Loop Optimization for Perceived Realism of Haptic Rendering.

Bilal Catkin, Volkan Patoglu

IEEE Transactions on Haptics

|April 13, 2023

Summary

This summary is machine-generated.

This study introduces preference-based human-in-the-loop (HiL) optimization to enhance haptic rendering realism. The approach efficiently optimizes system parameters for improved perceived realism in virtual interactions.

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Area of Science:

Human-Computer Interaction
Virtual Reality
Robotics

Background:

Haptic rendering fidelity, or perceived realism, is crucial for realistic virtual interactions.
Perceived realism is influenced by user's physical, mental, and perceptual characteristics.
Human-in-the-loop (HiL) studies enable simultaneous optimization of hardware, algorithms, and human factors in haptic systems.

Purpose of the Study:

To propose and evaluate preference-based HiL optimization for maximizing haptic rendering realism.
To investigate the efficiency of Bayesian optimization and pairwise comparisons in this context.
To optimize parameters for spring and friction rendering.

Main Methods:

Utilized sample-efficient Bayesian optimization algorithms for preference-based HiL optimization.
Employed qualitative pairwise comparisons to gather user feedback.
Conducted two HiL studies focusing on spring and friction rendering.
Validated optimized models against expert-tuned nominal models.

Main Results:

Demonstrated effective optimization of system parameters within a practical timeframe.
Showcased the approach's efficiency in studying haptic rendering parameter effects on perceived realism.
Captured parameter interactions, even in high-dimensional spaces.

Conclusions:

Preference-based HiL optimization is a viable and efficient method for enhancing haptic rendering realism.
This approach facilitates the study of complex parameter interactions influencing user perception.
Optimized haptic rendering leads to more immersive and realistic virtual experiences.