inverse-dynamics

This paper proposes a method to bridge the simulation-to-real-world gap in robotics by learning a deep inverse dynamics model that maps desired next states (from simulation) to appropriate real-world actions. The approach is evaluated against baselines like output error control and Gaussian dynamics adaptation.