Rapid aiming movement involves quick, accurate, pre-programmed motions used in the context of human-computer and human-robot interaction. It incorporates target forecasting to minimize the duration of tasks requiring rapid aiming. Applications include predicting target icons in UI design, driver intent in automotive technology, and human intent during human-robot collaboration. Conventional approaches often fail to capture human preferences accurately, leading to low prediction accuracy. This work explores an Inverse Reinforcement Learning (IRL)-based system for forecasting human hand movements and intended targets during rapid aiming. Sampling-based Maximum Entropy IRL (SMEIRL) with a sampler and Maximum Entropy Deep IRL (MEDIRL) algorithms were evaluated for prediction accuracy. The proposed sampler efficiently generates sample trajectories for rapid aiming tasks. User studies were conducted to assess target prediction during two tasks involving rapid aiming movement: (1) Pointing in Virtual Reality (VR) and Mixed Reality (MR), and (2) Human-robot handovers. A multimodal target prediction algorithm was analyzed for swift and accurate anticipation of the intended target, considering both hand and eye gaze. Results demonstrate that the proposed approach achieves a prediction accuracy of 98% in MR and 96% in VR for the pointing task using SMEIRL. During human-robot handover task, prediction accuracy using MEDIRL reached 99.9% when less than 20% and 40% of the task was left using only hand motion or both hand and eye gaze, respectively, surpassing state-of-the-art methods using Path Integral-IRL (PI-IRL), Recurrent Neural Network-Inverse Kinematics-Modified Kalman Filtering (RNNIK-MKF), Bayesian Predictor for Human Motion Trajectory (BP-HMT), and Classical kinematics of motion ( \(CM_{k=5}\) ).

In shared autonomy, human-robot handover for object delivery is crucial. Accurate robot predictions of human hand motion and intentions enhance collaboration efficiency. However, low prediction accuracy increases mental and physical demands on the user. In this work, we propose a system for predicting hand motion and intended target during human-robot handover using Inverse Reinforcement Learning (IRL). A set of feature functions were designed to explicitly capture users’ preferences during the task. The proposed approach was experimentally validated through user studies. Results indicate that the proposed method outperformed other state-of-the-art methods (PI-IRL, BP-HMT, RNNIK-MKF and CMk=5) with users feeling comfortable reaching upto 60% of the total distance to the target for handover with 90% target prediction accuracy. The target prediction accuracy reaches 99.9% when less than 20% of the task remains.