This course aims to teach learners about distributed perception and learning between robots and the cloud. The learning outcomes include understanding the key challenges of cloud robotics, distributed inference, and learning, as well as the accuracy and hidden costs associated with robot and cloud deep neural networks. The course covers topics such as network congestion, cloud offloading decision-making, reinforcement learning, and the benefits of specialization in correcting errors. The teaching method involves discussing theoretical concepts, practical experiments, and exploring real-world applications. This course is intended for individuals interested in robotics, cloud computing, artificial intelligence, and machine learning.