Multimodal Target Prediction for Rapid Human-Robot Interaction

Abstract

Intent prediction finds widespread applications in user interface (UI/UX) design to predict target icons, in automotive industry to anticipate driver's intent, and in understanding human motion during human-robot interactions (HRI). Predicting human intent involves analyzing factors such as hand motion, eye gaze movement, and gestures. This paper introduces a multimodal intent prediction algorithm involving hand and eye gaze using Bayesian fusion. Inverse reinforcement learning was leveraged to learn human preferences for the human-robot handover task. Results demonstrate that the proposed approach achieves the highest prediction accuracy of 99.9% at 60% task completion as compared to state-of-the-art (SOTA) methods.