Redirection Controller Using Reinforcement Learning
Redirection Controller Using Reinforcement Learning
复制标题
使用强化学习的重定向控制器
DOI：
10.1109/access.2021.3118056
复制
发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Hirose Michitaka
中科院分区：
文献类型：
--
作者：
Chang Yuchen;Matsumoto Keigo;Narumi Takuji;Tanikawa Tomohiro;Hirose Michitaka
对重定向行走(RDW)技术及其在各种虚拟和现实环境中的应用的需求日益增长。为了根据真实和虚拟环境应用适当的RDW方法和操作，RDW控制器是主要使用的。RDW控制器有三种：直接脚本式控制器、广义控制器和预测控制器。脚本化控制器类型预先编写了真实环境和虚拟环境之间的映射脚本。广义控制器类型根据用户相对于真实空间的位置根据特定过程使用RDW方法和操纵量。这种方法有可能在任何环境中重复使用；但是，它并没有完全优化。预测控制器类型使用用户的行为来预测用户的未来路径，并管理RDW技术。这一方法被高度期望是非常有效和通用的；然而，它还没有得到充分的发展。提出了一种基于强化学习(RL)的RDW控制器，该控制器具有良好的可规划性和通用性。仿真实验表明，在障碍较多的实际环境下，与广义控制器相比，该方法可以减少重置操作次数，这是衡量RDW控制器有效性的指标之一。同时，实验结果还表明，该方法输出的增益是振荡的。用户研究的结果表明，与传统的广义控制器相比，所提出的RDW控制器可以减少重置次数。此外，没有表现出与输出增益振荡相关的晕厥等不利影响。仿真和用户研究表明，具有RL的RDW控制器的性能优于现有的广义控制器，并且可以应用于用户，因为它只会引起与传统广义控制器相当的晕厥。
There is a growing demand for redirected walking (RDW) techniques and their application to various virtual and real environments. To apply appropriate RDW methods and manipulation according to the real and virtual environments, the RDW controllers are predominantly used. There are three types of RDW controllers: direct scripted controller, generalized controller, and predictive controller. The scripted controller type pre-scripts the mapping between the real and virtual environments. The generalized controller type employs the RDW method and manipulation quantities according to a certain procedure depending on the user’s position in relation to the real space. This approach has the potential to be reused in any environment; however, it is not fully optimized. The predictive controller type predicts the user’s future path using the user’s behavior and manages RDW techniques. This approach is highly anticipated to be very effective and versatile; however, it has not been sufficiently developed. This paper proposes a novel RDW controller using reinforcement learning (RL) with advanced plannability/versatility. Our simulation experiments indicate that the proposed method can reduce the number of reset manipulations, which is one of the indicators of the effectiveness of the RDW controller, compared to the generalized controller under real environments with many obstacles. Meanwhile, the experimental results also showed that the gain output by the proposed method oscillates. The results of a user study conducted showed that the proposed RDW controller can reduce the number of resets compared to the conventional generalized controller. Furthermore, no adverse effects such as cybersickness associated with the oscillation of the output gain were evinced. The simulation and user studies demonstrate that the proposed RDW controller with RL outperforms the existing generalized controllers and can be applied to users as it only causes cybersickness comparable to that in the case of conventional generalized controllers.