Sim-to-real transfer using deep inverse dynamics models

AI Impact Summary

This change introduces a capability to transfer control policies from simulated environments to real hardware by learning a deep inverse dynamics model. The approach aims to collapse the sim-to-real gap by mapping desired state transitions to corresponding actuator commands, potentially reducing real-world data requirements. To succeed, teams should pair high-fidelity simulations with domain randomization or system identification, and establish rigorous real-world validation and safety checks to detect model drift.