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

The fidelity of haptic rendering is characterized by the perceived realism capturing the level of similarity to a corresponding tangible object. Perceived realism depends on the musculoskeletal, mental, and perceptual properties of the individuals that manipulate the system. Human-in-the-loop (HiL) studies provide a feasible means for the concurrent optimization of the performance of the overall haptic rendering process, as the physical limitations of the hardware, the factors affecting the fidelity of the rendering algorithm, and the limitations of human action and perception can all be considered simultaneously. In this study, we propose the use of preference-based HiL optimization techniques based on sample-efficient Bayesian optimization algorithms and qualitative pairwise comparisons to maximize the perceived realism of haptic rendering tasks. We present two HiL optimization studies that maximize the perceived realism of spring and friction rendering and validate our results by comparing the HiL-optimized rendering models with expert-tuned nominal models. We show that the system parameters can effectively be optimized within a reasonable amount of time using a preference-based HiL optimization approach. Furthermore, we demonstrate that the approach provides an efficient means of studying the effect of haptic rendering parameters on perceived realism by capturing the interactions among the parameters, even for relatively high dimensional parameter spaces.