Assemble like expert: Multitask-meta hierarchical imitation learning algorithm guided by an expert skill model for robot polygonal peg-in-hole assembly

Robot polygonal peg-in-hole assembly is still challenging due to the unknown assembly environment and diverse tasks. To equip robots with expert assembly skills, this paper employs a model-guided strategy learning approach and proposes a multitask-meta hierarchical imitation learning algorithm guided by an expert skill model. Specifically, to construct a skill model for guiding strategy learning, a deterministic expert strategy is proposed. Based on this strategy, expert assembly characteristics are analyzed, and an expert skill model is developed to represent these characteristics. Furthermore, to learn experts' skill adjustment and generalization strategies across different tasks, a multitask-meta hierarchical imitation learning algorithm (MMHIL) is proposed. A parallel encoding attention network is designed to assist MMHIL in extracting multi-level skill information and learning assembly actions. A multitask-meta learning generalization framework with a mutual supervised learning optimization mechanism is proposed to enable MMHIL to rapidly adapt to new assembly tasks with limited training data. Comparative verification and polygonal peg-in-hole assembly experiments show that MMHIL has better skill learning effects and higher assembly success rates.