In this tutorial, you will build a practical Kotlin/Android component for a robotics or Physical AI system. The design emphasizes asynchronous processing, lifecycle-aware state, real-time data handling, observability, and safe separation between the Android interface and physical robot control.
Android Kotlin + Jetpack Compose
↓
ViewModel / Flow
↓
Repository / API
↓
ROS 2 / Jetson / AI Backend
↓
Robot System
data class Diagnostics(
val connected: Boolean,
val battery: Float,
val temperature: Float,
val cpu: Float,
val gpu: Float,
val lastMessageMs: Long
)
private val _diagnostics =
MutableStateFlow(Diagnostics(false, 0f, 0f, 0f, 0f, 0))
@Composable
fun DiagnosticCard(title: String, value: String) {
Card {
Column(Modifier.padding(12.dp)) {
Text(title)
Text(value)
}
}
}
val stale =
System.currentTimeMillis() - state.lastMessageMs > 3000
Store structured events such as connection changes, model failures, sensor errors, and watchdog activations.
For production systems, allow diagnostics to be exported for support and debugging, while avoiding sensitive information.
StateFlow for observable UI state.
The resulting Kotlin layer can be extended with real ROS 2 bridges, NVIDIA Jetson services, computer vision models, smart-glasses SDKs, or multimodal AI backends. Keep hardware-specific code behind interfaces so the Android application remains maintainable as the robotics stack evolves.
Website: www.v-modal.com
SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter
SDK Android: https://github.com/v-modal/vmodal_sdk_android
Discord: https://discord.gg/K72z28KUx