Unified Intention Inference and Learning for Human-Robot Cooperative Assembly

Collaborative robots are widely utilized in intelligent manufacturing to cooperate with the human to accomplish different assembly tasks. To improve the efficiency of human-robot cooperation, robots should be able to recognize human intentions and provide necessary assistance proactively. The major challenge for current human intention recognition methods is that they only deal with known human intentions of predefined tasks and lack of ability to learn unknown intentions corresponding to new tasks. This article introduces an evolving hidden Markov model (EHMM)-based approach to learn new human intentions incrementally by carrying out structure and parameter updating based on the observed sequence, in parallel with the recognition. The incremental learning ability makes it applicable in dynamic environments with changing tasks. A set of assistive execution policies has been developed for the robot to provide appropriate assistance to the human partner based on the intention recognition results in real time. Experiments have been carried out to verify the effectiveness of our approach in human-robot cooperative assembly tasks. The results show very high recognition accuracy (≥95.45%), and the human subjects show their high satisfaction with the intention learning ability of the proposed approach. Note to Practitioners - This article aims to effectively improve the productivity of human-robot cooperation by exploiting human adaptability and robot repeatability. Smooth cooperation requires the peer robot to provide proactive assistance to humans by inferring human intention after training. Moreover, the robot should also be able to learn untrained intentions online by human demonstrations. This is made possible by our proposed evolving hidden Markov model (EHMM) that unifies intention inference and incremental learning. Simplified cooperative assembly tasks have been designed to verify the proposed unified intention inference and learning model. A robotic assembly platform has been introduced to integrate the proposed EHMM with a perception module and a collaborative manipulation module. We have demonstrated, through experiments and surveys, that the proposed approach can promote efficacy and acceptance of human-robot cooperative assembly.