Robot learning towards smart robotic manufacturing: A review

• Published in: Robotics and computer-integrated manufacturing Vol. 77; p. 102360
• Main Authors: Liu, Zhihao, Liu, Quan, Xu, Wenjun, Wang, Lihui, Zhou, Zude
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2022; Elsevier BV
• Subjects: Artificial intelligence; Automation; Grasping (robotics); Industrial applications; Industrial robots; Machine learning; Manufacturing engineering; Production lines; Robot control; Robot learning; Robotic manufacturing; Robotics; Smart manufacturing; Robotic manufacturing; Robot learning; Artificial intelligence; Smart manufacturing
• ISSN: 0736-5845; 1879-2537; 1879-2537
• DOI: 10.1016/j.rcim.2022.102360

•This review introduced the comprehensive background of smart robotic manufacturing.•It delivered technical analysis on different robot learning methods.•Typical industrial applications with robot learning were listed and discussed.•Open problems and future research directions were summarised. Robotic equipment has been playing a central role since the proposal of smart manufacturing. Since the beginning of the first integration of industrial robots into production lines, industrial robots have enhanced productivity and relieved humans from heavy workloads significantly. Towards the next generation of manufacturing, this review first introduces the comprehensive background of smart robotic manufacturing within robotics, machine learning, and robot learning. Definitions and categories of robot learning are summarised. Concretely, imitation learning, policy gradient learning, value function learning, actor-critic learning, and model-based learning as the leading technologies in robot learning are reviewed. Training tools, benchmarks, and comparisons amongst different robot learning methods are delivered. Typical industrial applications in robotic grasping, assembly, process control, and industrial human-robot collaboration are listed and discussed. Finally, open problems and future research directions are summarised.