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Citation:

Dong-Yong Lee, Yong-Hun Cho, and and In-Kwon Lee, "Real-time Optimal Planning for Redirected Walking Using Deep Q-Learning", IEEE VR 2019 (The 26th IEEE Conference on Virtual Reality and 3D User Interfaces), March 2019

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Abstract:

This work presents a novel control algorithm of redirected walking called steer-to-optimal-target (S2OT) for effective real-time planning in redirected walking. S2OT is a method of redirection estimating the optimal steering target that can avoid the collision on the future path based on the user’s virtual and physical paths. We design and train the machine learning model for estimating optimal steering tar- get through reinforcement learning, especially, using the technique called Deep Q-Learning. S2OT significantly reduces the number of resets caused by collisions between user and physical space bound- aries compared to well-known algorithms such as steer-to-center (S2C) and Model Predictive Control Redirection (MPCred). The results are consistent for any combinations of room-scale and large- scale physical spaces and virtual maps with or without predefined paths. S2OT also has a fast computation time of 0.763 msec per redirection, which is sufficient for redirected walking in real-time environments. (BK21-accredited and KRF-selected Excellent International Conference IF=2)