Comparison of Target Prediction in VR and MR using Inverse Reinforcement Learning

Numerous research were undertaken to predict pointing targets in Graphical User Interfaces (GUI). This paper extends target prediction for Extended reality (XR) platforms through Sampling-based Maximum Entropy Inverse Reinforcement Learning (SMEIRL). The SMEIRL algorithm learns the underlying reward distribution for the pointing task. Results show that SMEIRL achieves better accuracy in both VR and MR (for example accuracy in VR and accuracy in MR at of pointing task) compared to Artificial Neural Network (ANN) and Quadratic Extrapolation (QE) during early stage of pointing task. For later stage, QE performs better (for example accuracy in VR and accuracy in MR at of the pointing task) than SMEIRL and ANN. All the three algorithms, SMEIRL, ANN and QE reported higher target prediction accuracy in MR than in VR.