Adaptive Meta Policy Learning With Virtual Model for Multi-Category Peg-in-Hole Assembly Skills

The generalization model for multicategory peg-in-hole assembly (MPHA) skills is hard to acquire. An adaptive meta policy learning (AMPL) algorithm with virtual model set is proposed to deal with the difficulties of low learning efficiency and low adaptability for the multicategory assembly skill learning. First, the AMPL framework incorporates meta-reinforcement learning and can obtain generalization model of multicategory assembly skills. It has higher learning efficiency compared to single-category skill learning algorithms. Second, the AMPL algorithm introduces a similarity function constructed from the demonstration learning algorithm in the state value function. It has stronger adaptability compared to the multicategory skills learning algorithms. Finally, a simulation environment set for MPHA is constructed, consisting of the mathematical force contact models and the physical simulation models. The simulations and experiments are well conducted with the proposed algorithm. The results demonstrate the efficacy of the proposed algorithm.