Incremental Motor Skill Learning and Generalization From Human Dynamic Reactions Based on Dynamic Movement Primitives and Fuzzy Logic System
Zhenyu Lu, Ning Wang, Miao Li, Chenguang Yang
Abstract
Different from previous work on single skill learning from human demonstrations, an incremental motor skill learning, generalization and control method based on dynamic movement primitives (DMP) and broad learning system (BLS) is proposed for extracting both ordinary skills and instant reactive skills from demonstrations, the latter of which is usually generated to avoid a sudden danger (e.g., touching a hot cup). The method is completed in three steps. First, the ordinary skills are basically learned from demonstrations in normal cases by using DMP. Then, the incremental learning idea of BLS is combined with DMP to achieve multistylistic reactive skill learning such that the forcing function of the ordinary skills will be reasonably extended into multiple stylistic functions by adding enhancement terms and updating weights of the radial basis function kernels. Finally, electromyography signals are collected from human muscles and processed to achieve stiffness factors. By using fuzzy logic system, the two kinds of skills learned are integrated and generalized in new cases such that not only start, end and scaling factors but also the environmental conditions, robot reactive strategies and impedance control factors will be generalized to lead to various reactions. To verify the effectiveness of the proposed method, an obstacle avoidance experiment that enables robots to approach destinations flexibly in various situations with barriers will be undertaken.