Sim-to-real NavigationExpert
EIDA: Execution-Interface Dynamics Adaptation for Real-to-Sim-to-Real Robot Navigation
EIDA is a preprint that learns execution-interface dynamics models from real robot velocity-command, pose, and feedback data and embeds them into a lightweight GPU simulator for navigation policy training. On a physical Unitree Go2 it reached the goal without collision in 20/20 static-scene trials versus 4/20 for a standard simulator-trained baseline, and it led learned policies on 100 BARN environments with 89.33% success under global guidance versus 80.78% for Nominal. It offers a cheaper, platform-specific sim-to-real transfer route without detailed actuator modeling.