Sensorimotor Simulation of Redirected Reaching using Stochastic Optimal Feedback Control

要旨

Illusory VR interaction techniques such as hand redirection work because humans use vision to adjust their motor commands during movement (e.g., reaching). Existing simulations of redirected reaching are limited, however, and have not yet incorporated important stochastic characteristics like sensorimotor noise, nor captured redirection's effect on movement duration. In this work, we propose adapting a stochastic optimal feedback control (SOFC) model of normal reach to simulate redirection by augmenting sensory feedback at run-time. We present a summary of our simulation and validate it against user data gathered in multiple redirection conditions. We also evaluate the impacts of visual attention on the effectiveness of redirection in real users and replicate the effects in simulation. Our results show that an infinite-horizon SOFC model is able to reproduce key characteristics of redirected reaches and highlight the benefits of SOFC as a tool for simulating, evaluating, and gaining insights about redirection techniques.

受賞
Best Paper
著者
Eric J. Gonzalez
Stanford University, Stanford, California, United States
Sean Follmer
Stanford University, Stanford, California, United States
論文URL

https://doi.org/10.1145/3544548.3580767

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: User Behavior Simulation and Modeling

Hall G2
6 件の発表
2023-04-27 18:00:00
2023-04-27 19:30:00