Axite AI: A Python Agent for Sim2Real Sensor Validation

In the winter of 2025, I developed Axite AI, a Python-based software agent designed to validate simulated robotics environments against real-world sensor data. The core of the project was to create a seamless workflow for logging, synchronizing, and analyzing sensor streams for sim-to-real differential analysis.

The Problem

One of the biggest challenges in robotics is the “sim-to-real gap,” where behaviors that work perfectly in a simulation fail in the real world. This is often due to subtle differences in sensor readings. Axite AI was built to identify these discrepancies automatically.

The Implementation

The system consisted of a lightweight Python agent that attaches to a robot’s onboard computer. Its primary responsibilities were:

1. Sensor Interfacing: The agent interfaced with various sensors, including the IMU, LiDAR, and motor encoders.
2. Real-time Logging: It used the Rerun SDK to log and stream structured sensor data in real time. This allowed for live visualization of the robot’s state.
3. Data Synchronization: The agent streamed the data via WebSockets to a server running on AWS, where it was synchronized with corresponding simulation data.

On the server, an automated process performed differential comparisons between the simulated and real-world trajectories. It flagged any deviations that exceeded configurable thresholds, providing valuable feedback to refine the robot’s control policies and simulation parameters.

Tech Stack

• Core Language: Python
• Visualization & Logging: Rerun
• Communication: WebSockets
• Cloud Infrastructure: AWS
• Containerization: Docker

This project was a deep dive into robotics, data streaming, and the challenges of bridging the gap between simulation and reality.