DeepRIoT: Continuous Integration and Deployment of Robotic-IoT Applications

Abstract

We present DeepRIoT, a continuous integration and continuous deployment (CI/CD) based architecture that accelerates the learning and deployment of a Robotic-IoT system trained from deep reinforcement learning (RL). We adopted a multi-stage approach that agilely trains a multi-objective RL controller in the simulator. We then collected traces from the real robot to optimize its plant model, and used transfer learning to adapt the controller to the updated model. We automated our framework through CI/CD pipelines, and finally, with low cost, succeeded in deploying our controller in a real F1tenth car that is able to reach the goal and avoid collision from a virtual car through mixed reality.