Abstract
Redirected walking (RDW) is a locomotion technique used in virtual reality (VR) that enables users to explore large virtual environments in a limited physical space. Existing RDW techniques mainly work on the obstacle-free physical spaces larger than a square of four-meter sides. To improve usability, RDW techniques that work on comparatively smaller physical spaces with obstacles need to be developed. In RDW, users are restricted to the physical space by redirection techniques (RETs) that control the view of the head-mounted display. Reinforcement learning, a branch of machine learning techniques, is advantageous in designing efficient redirection controllers compared to manual design. In this paper, we propose a reinforcement learning-based redirection controller (RLRC) that aims to realize an efficient RDW in small physical spaces. The controller is trained using the simulator and is expected to select an appropriate redirection technique from the current state and route information of the virtual environment. We evaluate the RLRC with simulator and user tests in a virtual maze in several physical spaces, including a square physical space of four-meter sides with an obstacle, and a square physical space of two-meter sides. The simulator test shows that the proposed RLRC can reduce the number of undesirable redirection techniques performed compared with existing methods. The proposed RLRC is found to be effective in the square physical space of two-meter sides in the user test.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chang, Y., Matsumoto, K., Narumi, T., Tanikawa, T., Hirose, M.: Redirection controller using reinforcement learning. arXiv preprint arXiv:1909.09505 (2019)
Chen, H., Chen, S., Rosenberg, E.S.: Redirected walking in irregularly shaped physical environments with dynamic obstacles. In: 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 523–524 (2018)
Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv. Psychol. 52, 139–183 (1988)
Hasselt, H.V., Guez, A., Silver, D.: Deep reinforcement learning with double Q-learning. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Juliani, A., et al.: Unity: A general platform for intelligent agents. arXiv preprint arXiv:1809.02627 (2018)
Kennedy, R.S., Lane, N.E., Berbaum, K.S., Lilienthal, M.G.: Simulator sickness questionnaire: an enhanced method for quantifying simulator sickness. Int. J. Aviat. Psychol. 3(3), 203–220 (1993)
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: 1985 IEEE International Conference on Robotics and Automation, vol. 2, pp. 500–505 (1985)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)
Lee, D., Cho, Y., Lee, I.: Real-time optimal planning for redirected walking using deep Q-learning. In: 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 63–71 (2019)
Nescher, T., Huang, Y.Y., Kunz, A.: Planning redirection techniques for optimal free walking experience using model predictive control. In: 2014 IEEE Symposium on 3D User Interfaces (3DUI), pp. 111–118 (2014)
Ramachandran, P., Zoph, B., Le, Q.V.: Swish: a self-gated activation function. arXiv preprint arXiv:1710.05941 (2017)
Razzaque, S., Kohn, Z., Whitton, M.C.: Redirected walking. In: Eurographics 2001 - Short Presentations. Eurographics Association (2001)
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)
Steinicke, F., Bruder, G., Jerald, J., Frenz, H., Lappe, M.: Estimation of detection thresholds for redirected walking techniques. IEEE Trans. Visual Comput. Graphics 16(1), 17–27 (2010)
Strauss, R.R., Ramanujan, R., Becker, A., Peck, T.C.: A steering algorithm for redirected walking using reinforcement learning. IEEE Trans. Visual Comput. Graphics 26(5), 1955–1963 (2020)
Thomas, J., Rosenberg, E.S.: A general reactive algorithm for redirected walking using artificial potential functions. In: 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 56–62 (2019)
Williams, B., et al.: Exploring large virtual environments with an HMD when physical space is limited. In: 4th Symposium on Applied Perception in Graphics and Visualization (APGV 2007), pp. 41–48 (2007)
Zmuda, M.A., Wonser, J.L., Bachmann, E.R., Hodgsons, E.: Optimizing constrained-environment redirected walking instructions using search techniques. IEEE Trans. Visual Comput. Graphics 19(11), 1872–1884 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Shibayama, W., Shirakawa, S. (2020). Reinforcement Learning-Based Redirection Controller for Efficient Redirected Walking in Virtual Maze Environment. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2020. Lecture Notes in Computer Science(), vol 12221. Springer, Cham. https://doi.org/10.1007/978-3-030-61864-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-61864-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61863-6
Online ISBN: 978-3-030-61864-3
eBook Packages: Computer ScienceComputer Science (R0)