Target prediction or intent recognition has been studied for many applications ranging from aviation, social computing, human machine interaction and so on. At its simplest form, target prediction may take the form of linear extrapolation to more complex activity of intent recognition or behaviour forecasting in the domain of social computing. Our present research investigates multimodal target prediction for human robot handover requests. Following Newell’s time scale of human actions, the prediction worked at the cognitive band or at the range of 1/10th to tens of seconds. This talk will explore different technologies and applications for human movement prediction ranging from finger movement to walking trajectories. The talk will start with an application of using artificial neural network-based target prediction for different input modalities to reduce pointing time in a GUI (Graphical User Interface). Next, I move on to using and comparing different Imitation Learning based algorithms like Inverse Reinforcement Learning and Behavior Cloning to predict target for a human robot handover task and combining hand trajectory with eye gaze fixation. Finally, a particle filter based approach will be discussed for walking trajectory prediction for a VR digital twin of office space. Speaker's Bio:
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