Anselmo Parnada

Disturbance-Augmented Simulations for Improved Sim-to-Real Transfer of Compliant Reinforcement Learning-Based Robotic Control

Sim-to-real transfer enables scalable acquisition of compliant robotic skills for contact-rich tasks, yet dynamics mismatches between simulation and hardware hinder reliable deployment. Prior approaches assume disturbance-free training dynamics, neglecting endogenous disturbances from nominalplant modelling errors that...