A Steering Algorithm for Redirected Walking Using Reinforcement Learning

Redirected Walking (RDW) steering algorithms have traditionally relied on human-engineered logic. However, recent advances in reinforcement learning (RL) have produced systems that surpass human performance on a variety of control tasks. This paper investigates the potential of using RL to develop a...

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Vydáno v:	IEEE transactions on visualization and computer graphics Ročník 26; číslo 5; s. 1955 - 1963
Hlavní autoři:	Strauss, Ryan R., Ramanujan, Raghuram, Becker, Andrew, Peck, Tabitha C.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.05.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Artificial neural networks Computer Graphics Computer simulation Control tasks Deep Learning Heuristic algorithms Human performance Humans Learning (artificial intelligence) Legged locomotion Locomotion Machine learning Meters Prediction algorithms Redirected Walking Reinforcement Learning Space exploration Steering Steering Algorithms Tracking Video Games Virtual environments Virtual Reality Walking Walking - physiology
ISSN:	1077-2626, 1941-0506, 1941-0506
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Abstract	Redirected Walking (RDW) steering algorithms have traditionally relied on human-engineered logic. However, recent advances in reinforcement learning (RL) have produced systems that surpass human performance on a variety of control tasks. This paper investigates the potential of using RL to develop a novel reactive steering algorithm for RDW. Our approach uses RL to train a deep neural network that directly prescribes the rotation, translation, and curvature gains to transform a virtual environment given a user's position and orientation in the tracked space. We compare our learned algorithm to steer-to-center using simulated and real paths. We found that our algorithm outperforms steer-to-center on simulated paths, and found no significant difference on distance traveled on real paths. We demonstrate that when modeled as a continuous control problem, RDW is a suitable domain for RL, and moving forward, our general framework provides a promising path towards an optimal RDW steering algorithm.
AbstractList	Redirected Walking (RDW) steering algorithms have traditionally relied on human-engineered logic. However, recent advances in reinforcement learning (RL) have produced systems that surpass human performance on a variety of control tasks. This paper investigates the potential of using RL to develop a novel reactive steering algorithm for RDW. Our approach uses RL to train a deep neural network that directly prescribes the rotation, translation, and curvature gains to transform a virtual environment given a user's position and orientation in the tracked space. We compare our learned algorithm to steer-to-center using simulated and real paths. We found that our algorithm outperforms steer-to-center on simulated paths, and found no significant difference on distance traveled on real paths. We demonstrate that when modeled as a continuous control problem, RDW is a suitable domain for RL, and moving forward, our general framework provides a promising path towards an optimal RDW steering algorithm. Redirected Walking (RDW) steering algorithms have traditionally relied on human-engineered logic. However, recent advances in reinforcement learning (RL) have produced systems that surpass human performance on a variety of control tasks. This paper investigates the potential of using RL to develop a novel reactive steering algorithm for RDW. Our approach uses RL to train a deep neural network that directly prescribes the rotation, translation, and curvature gains to transform a virtual environment given a user's position and orientation in the tracked space. We compare our learned algorithm to steer-to-center using simulated and real paths. We found that our algorithm outperforms steer-to-center on simulated paths, and found no significant difference on distance traveled on real paths. We demonstrate that when modeled as a continuous control problem, RDW is a suitable domain for RL, and moving forward, our general framework provides a promising path towards an optimal RDW steering algorithm.Redirected Walking (RDW) steering algorithms have traditionally relied on human-engineered logic. However, recent advances in reinforcement learning (RL) have produced systems that surpass human performance on a variety of control tasks. This paper investigates the potential of using RL to develop a novel reactive steering algorithm for RDW. Our approach uses RL to train a deep neural network that directly prescribes the rotation, translation, and curvature gains to transform a virtual environment given a user's position and orientation in the tracked space. We compare our learned algorithm to steer-to-center using simulated and real paths. We found that our algorithm outperforms steer-to-center on simulated paths, and found no significant difference on distance traveled on real paths. We demonstrate that when modeled as a continuous control problem, RDW is a suitable domain for RL, and moving forward, our general framework provides a promising path towards an optimal RDW steering algorithm.
Author	Ramanujan, Raghuram Strauss, Ryan R. Peck, Tabitha C. Becker, Andrew
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Snippet	Redirected Walking (RDW) steering algorithms have traditionally relied on human-engineered logic. However, recent advances in reinforcement learning (RL) have...
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SubjectTerms	Algorithms Artificial neural networks Computer Graphics Computer simulation Control tasks Deep Learning Heuristic algorithms Human performance Humans Learning (artificial intelligence) Legged locomotion Locomotion Machine learning Meters Prediction algorithms Redirected Walking Reinforcement Learning Space exploration Steering Steering Algorithms Tracking Video Games Virtual environments Virtual Reality Walking Walking - physiology
Title	A Steering Algorithm for Redirected Walking Using Reinforcement Learning
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