Deep learning (DL) has been widely investigated and applied for frontier mobile robotics and smart machinery. Broad learning system (BLS) has been shown to work as an effective and efficient incremental learning without the need for deep architecture, thus giving a new paradigm and learning system for AI systems. By incorporating the merits of DL, BLS and intelligent motion control methods, this talk will present you DL-based or BLS-based novel control frameworks or paradigms for a class of mobile robots, multirobots and smart machinery, in order to achieve desired control. In the short talk, some literature reviews about DLs and BLs are first mentioned, and then four novel DL-based or BL-based control methods are briefly highlighted and demonstrated for their applications to intelligent mobile robots, multirobots and collaborative air-ground robotic　systems. Last but not least, some perspective topics are recommended for future research.

Autonomous Navigation， Deep Learning, Broad Learning System, Intelligent Robots, Intelligent Machinery