Redirection controller-based reinforcement learning for redirected walking

Jianyu Zhao; Wenzhe Zhu; Qing Zhu

doi:10.1117/12.2684589

19 October 2023 Redirection controller-based reinforcement learning for redirected walking

Jianyu Zhao, Wenzhe Zhu, Qing Zhu

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270930 (2023) https://doi.org/10.1117/12.2684589
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Redirected Walking (RDW) technique allows users to walk indefinitely in a limited physical space while keeping the feeling of real walking. Developers use RDW controllers to manage RDW techniques based on physical and virtual environment information. However, traditional RDW controllers still suffer from many problems. For example, generalized controllers are less optimized, and scripted controllers are difficult to handle unexpected movements. Based on reinforcement learning, we present a novel RDW controller that allows the user to explore complex and large virtual environments while minimizing the number of collisions with obstacles in the physical environments. Our RDW controller directly prescribes the translation, rotation, and curvature gains by analyzing real-time information of the physical environment. The simulation-based experiments show that our controller significantly reduces the number of resets caused by collisions between user and obstacles of physical spaces compared to steer-to-center (S2C) and current state-of-the-art controllers using reinforcement learning.

Citation Download Citation

Jianyu Zhao, Wenzhe Zhu, and Qing Zhu "Redirection controller-based reinforcement learning for redirected walking", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270930 (19 October 2023); https://doi.org/10.1117/12.2684589

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