IRL based Hand Movement Prediction for XR Interaction

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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 32.60% accuracy in VR and 34.48% accuracy in MR at 30% 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 93.51% accuracy in VR and 95.58% accuracy in MR at 70% 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.