With the rapid advancement of manufacturing in China,robot machining technology has become a popular research subject.An increasing number of robots are currently being used to perform complex tasks during manual oper...With the rapid advancement of manufacturing in China,robot machining technology has become a popular research subject.An increasing number of robots are currently being used to perform complex tasks during manual operation,e.g.,the grinding of large components using multi-robot systems and robot teleoperation in dangerous environments,and machining conditions have evolved from a single open mode to a multisystem closed mode.Because the environment is constantly changing with multiple systems interacting with each other,traditional methods,such as mechanism modeling and programming are no longer applicable.Intelligent learning models,such as deep learning,transfer learning,reinforcement learning,and imitation learning,have been widely used;thus,skill learning and strategy optimization have become the focus of research on robot machining.Skill learning in robot machining can use robotic flexibility to learn skills under unknown working conditions,and machining strategy research can optimize processing quality under complex working conditions.Additionally,skill learning and strategy optimization combined with an intelligent learning model demonstrate excellent performance for data characteristics learning,multisystem transformation,and environment perception,thus compensating for the shortcomings of the traditional research field.This paper summarizes the state-of-the-art in skill learning and strategy optimization research from the perspectives of feature processing,skill learning,strategy,and model optimization of robot grinding and polishing,in which deep learning,transfer learning,reinforcement learning,and imitation learning models are integrated into skill learning and strategy optimization during robot grinding and polishing.Finally,this paper describes future development trends in skill learning and strategy optimization based on an intelligent learning model in the system knowledge transfer and nonstructural environment autonomous processing.展开更多
In my ten-yeat experience of tenching English as major in college , I feel that students experience more difficulties in listening lessons than in other lessons. What, then, prevents students from achieving success in...In my ten-yeat experience of tenching English as major in college , I feel that students experience more difficulties in listening lessons than in other lessons. What, then, prevents students from achieving success in the listening class? I suggest that anxiety is one of the major factors, which interferes with students listening comprehension. And I would agree with writers such as Gardner and Horwitz et al. (1991)that anxiety may impede learner’s ability to perform successfully in all aspects of learning a foreign language. I feet it is particularly significant for listening.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52105515&52188102)the Joint Fund of the Hubei Province of China(Grant No.U20A20294)。
文摘With the rapid advancement of manufacturing in China,robot machining technology has become a popular research subject.An increasing number of robots are currently being used to perform complex tasks during manual operation,e.g.,the grinding of large components using multi-robot systems and robot teleoperation in dangerous environments,and machining conditions have evolved from a single open mode to a multisystem closed mode.Because the environment is constantly changing with multiple systems interacting with each other,traditional methods,such as mechanism modeling and programming are no longer applicable.Intelligent learning models,such as deep learning,transfer learning,reinforcement learning,and imitation learning,have been widely used;thus,skill learning and strategy optimization have become the focus of research on robot machining.Skill learning in robot machining can use robotic flexibility to learn skills under unknown working conditions,and machining strategy research can optimize processing quality under complex working conditions.Additionally,skill learning and strategy optimization combined with an intelligent learning model demonstrate excellent performance for data characteristics learning,multisystem transformation,and environment perception,thus compensating for the shortcomings of the traditional research field.This paper summarizes the state-of-the-art in skill learning and strategy optimization research from the perspectives of feature processing,skill learning,strategy,and model optimization of robot grinding and polishing,in which deep learning,transfer learning,reinforcement learning,and imitation learning models are integrated into skill learning and strategy optimization during robot grinding and polishing.Finally,this paper describes future development trends in skill learning and strategy optimization based on an intelligent learning model in the system knowledge transfer and nonstructural environment autonomous processing.
文摘In my ten-yeat experience of tenching English as major in college , I feel that students experience more difficulties in listening lessons than in other lessons. What, then, prevents students from achieving success in the listening class? I suggest that anxiety is one of the major factors, which interferes with students listening comprehension. And I would agree with writers such as Gardner and Horwitz et al. (1991)that anxiety may impede learner’s ability to perform successfully in all aspects of learning a foreign language. I feet it is particularly significant for listening.
