OpenRDW: A Redirected Walking Library and Benchmark with Multi-User, Learning-based Functionalities and State-of-the-art Algorithms

Redirected walking (RDW) is a locomotion technique that guides users on virtual paths, which might vary from the paths they physically walk in the real world. Thereby, RDW enables users to explore a virtual space that is larger than the physical counterpart with near-natural walking experiences. Several approaches have been proposed and developed; each using individual platforms and evaluated on a custom dataset, making it challenging to compare between methods. However, there are seldom public toolkits and recognized benchmarks in this field. In this paper, we introduce OpenRDW, an open-source library and benchmark for developing, deploying and evaluating a variety of methods for walking path redirection. The OpenRDW library provides application program interfaces to access the attributes of scenes, to customize the RDW controllers, to simulate and visualize the navigation process, to export multiple formats of the results, and to evaluate RDW techniques. It also supports the deployment of multi-user real walking, as well as reinforcement learning-based models exported from TensorFlow or PyTorch. The OpenRDW benchmark includes multiple testing conditions, such as walking in size varied tracking spaces or shape varied tracking spaces with obstacles, multiple user walking, etc. On the other hand, procedurally generated paths and walking paths collected from user experiments are provided for a comprehensive evaluation. It also contains several classic and state-of-the-art RDW techniques, which include the above mentioned functionalities.