Embodied Collaborative Navigation and Interactive Learning

At present, complex and highly dynamic environments have put forward more urgent demands on robot perception and learning. Cluster systems have brought efficiency multiplication and application breakthroughs to multi-robot collaborative applications, but also brought huge challenges to the theoretical research and engineering application of group intelligence perception and learning. This report focuses on the two major multi-robot collaborative task requirements of situation understanding in adapting to the differences in the perception and action capabilities of heterogeneous robot platforms, and efficient and robust perception in wide-area dynamic scenes. It introduces the relevant research progress on how to use the perception and learning capabilities of heterogeneous multi-robots to achieve cluster efficiency and behavior emergence.