Kotlin and Edge AI Combined to Cut Inference Latency in Android Robotics Systems

A new tutorial published on DEV Community walks developers through building a Kotlin and Android component designed for robotics and Physical AI applications. The guide covers asynchronous processing, lifecycle-aware state management, and real-time data handling using tools like Jetpack Compose, ViewModel, and Kotlin Flow. A key focus is measuring end-to-end pipeline latency rather than relying solely on a model's benchmark inference time. The tutorial recommends techniques such as frame sampling, buffer reuse, hardware acceleration, and model quantization to reduce latency across the full stack. The resulting component is designed to integrate with ROS 2 bridges, NVIDIA Jetson services, and multimodal AI backends while keeping safety controls independent of high-bandwidth data streams.