Enhanced Human-Robot Collaboration with Intent Prediction using Deep Inverse Reinforcement Learning

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 CM
k=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.