Hierarchical Human Motion Intention Prediction for Increasing Efficacy of Human-Robot Collaboration

When humans and robots work together to accomplish tasks with dynamic uncertainty, the robots should perceive human motion intentions so as to cooperate with humans and increase efficacy. In this study, we propose a hierarchical motion intention prediction model for human-robot collaboration, in which the bottom level acquires human motion information, the middle level recognizes motion states and the high level predicts motion intentions. Compared with existing methods, our model fuses task-level human behavioral pattern prediction with instantaneous continuous motion intent decoding. Therefore, the robot controller can generate a collaborative trajectory in advance and adjust the key parameters (forces and velocities, etc.) in real time according to human motions. We quantitatively verify the proposed model with 10 subjects in the human-robot sawing task. The results show that the hierarchical model can effectively reduce human energy consumption and improve the average speed of the task. Meanwhile, subjective metrics indicate that subjects believe robots employing hierarchical models as capable of fostering improved cooperation and delivering greater assistance. Our study systematically proves that the proposed hierarchical model significantly enhanced the efficiency of human-robot co-manipulation, marking a step forward compared with existing works. Future studies will be focused on investigating more complex and general tasks.