sim-to-real

Sim-to-real transfer bridges the gap between simulated and physical environments by applying domain randomization, sensor calibration, and latency handling. Deploy your Gazebo-trained model to edge hardware like Jetson Orin Nano by tuning parameters, validating with real sensors, and iterating with debugging tools like rviz and rqt_plot.

Domain randomization randomly varies simulation parameters—lighting, terrain, sensor properties—during training to create robust models that work on physical hardware despite real-world variability. It reduces the sim-to-real gap by preventing overfitting to simulation artifacts.

Yes. Transfer Nav2 algorithms by addressing latency on edge hardware, calibrating the RealSense D435i depth sensor, tuning locomotion parameters for the Unitree Go2, and validating in obstacle-rich environments with real-world verification and logging.

Sim-to-real addresses deployment across NVIDIA Jetson Orin Nano edge AI, Unitree Go2 quadruped, and Intel RealSense D435i sensors. Supported simulators include Gazebo, Unity, and Isaac Sim for training robust perception and locomotion models.

Calibrate real sensors like the RealSense D435i to match simulation assumptions, then measure and compensate for hardware latency during control loops. Test iteratively on the target platform, using visualization tools and logging to identify and resolve deployment mismatches.

Sim-to-real deployment faces sensor drift under varying lighting, latency variability on edge hardware, terrain inconsistency, and safety constraints. Address these through domain randomization, robust sensor fusion, parameter tuning, safety protocols, and iterative real-world verification with debugging tools.