Building a Kotlin Dashboard for NVIDIA Physical AI
Building a Kotlin Dashboard for NVIDIA Physical AI What You Will Build By the end of this tutorial, you will have a Kotlin-based architecture for the selected robotics/XR/AI scenario, with lifecycle-aware state, asynchronous processing, bounded data flow, monitoring, and practical safety handling. Topic focus: Physical AI dashboard Expose pipeline metrics such as perception time, planning time,…
Building a Kotlin Dashboard for NVIDIA Physical AI
This tutorial outlines how to create a Kotlin-based architecture for a robotics, XR, smart-glasses, or edge-AI system. The goal is to expose pipeline metrics such as perception time, planning time, control-loop time, camera status, model status, battery, and connection state. These metrics assist operators in diagnosing issues without moving the workload to Android.
The tutorial covers the following steps:
1. Create a new Kotlin Android application in Android Studio and enable Jetpack Compose.
2. Define a data model, `DeviceStatus`, with properties for connection status, battery level, and latency.
3. Create a lifecycle-aware state class, `DeviceViewModel`, using `MutableStateFlow` to maintain the device status.
4. Offload heavy processing to a background thread using `viewModelScope` and `Dispatchers.Default` to ensure UI remains responsive.
5. Collect the device state in Compose using `collectAsStateWithLifecycle()` to update the UI with the latest device state.
6. Implement bounded real-time processing using a `Channel` with a capacity of 2 to avoid unbounded queues for high-rate streams.
7. Sample telemetry data at regular intervals using `sample(100)` and call `updateUi()` to update the UI with new data.
8. Measure performance metrics, including CPU usage, memory consumption, frame time, network latency, and battery impact.
9. Add safety and failure handling to prevent the robot from continuing motion in case of a lost connection or other issues. Implement connection monitoring and a robot-side watchdog to ensure safe operation.
By following these steps, developers can build a practical Kotlin/Android layer for robotics, XR, smart-glasses, or edge-AI systems. This architecture enables easy monitoring of pipeline metrics and ensures efficient, safe, and responsive operation of the system.
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