Scalable Physics-Embedded Neural Networks for Real-Time Robotic Control in Embedded Systems

Physics-embedded neural networks have recently gained significant interest in robotics due to the benefits of combining data-driven machine learning approaches with physics-based modeling methods for real-time control. Despite the improved accuracy over black-box neural networks, existing works have limitations in handling large ranges of system parameters, extracting latent physical parameters, and meeting real-time latency constraints. This paper proposes enhanced physics-embedded neural network models that overcome the scaling limitation of existing models and coupling issues in the extraction of hidden variables, rendering significantly improved model accuracy by more than 95%. A reinforcement learning based neural architecture search engine is developed to meet real-time latency constraints in embedded microprocessors, optimize the solution for scaling issues, and enable the efficient deployment of physics-embedded neural networks into resource-limited edge devices, with 3X searching speed compared with the exhaustive random search method.