A Model Predictive Control Approach for Reach Redirection in Virtual Reality

要旨

Reach redirection is an illusion-based virtual reality (VR) interaction technique where a user’s virtual hand is shifted during a reach in order to guide their real hand to a physical location. Prior works have not considered the underlying sensorimotor processes driving redirection. In this work, we propose adapting a sensorimotor model for goal-directed reach to obtain a model for visually-redirected reach, specifically by incorporating redirection as a sensory bias in the state estimate used by a minimum jerk motion controller. We validate and then leverage this model to develop a Model Predictive Control (MPC) approach for reach redirection, enabling the real-time generation of spatial warping according to desired optimization criteria (e.g., redirection goals) and constraints (e.g., sensory thresholds). We illustrate this approach with two example criteria -- redirection to a desired point and redirection along a desired path -- and compare our approach against existing techniques in a user evaluation.

著者
Eric J. Gonzalez
Stanford University, Stanford, California, United States
Elyse D. Z.. Chase
Stanford University, Stanford, California, United States
Pramod Kotipalli
Stanford University, Stanford, California, United States
Sean Follmer
Stanford University, Stanford, California, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3501907

動画

会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)

セッション: Predictive Modelling and Simulating Users

291
5 件の発表
2022-05-03 01:15:00
2022-05-03 02:30:00