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.

The force-based control algorithm of robotic multiple peg-in-hole assembly is a challenge. For the difficulty of low adaptability of model-based control algorithms and low learning efficiency of model-free control algorithms, a goal-based hierarchical policy learning (HPL) algorithm that combines conventional control algorithm and demonstration learning (DL) algorithm is proposed to learn the assembly skill. First, the goal-based HPL algorithm adds goal as a new variable to the action value function. Multiple states reached in each episode are randomly selected as subgoals to improve the distribution of positive rewards. Second, an initial policy that combines conventional control algorithm and DL algorithm is designed. The combined coefficient of these two algorithms is learned by HPL algorithm. Finally, a conical surface is used to compute the forces and moments of simplified assembly simulation model. Our algorithm is well implemented in both simulation and real-world environments. The experimental results verify the effectiveness of the proposed method.