Sim-to-Real RL for ASVs using SysID
The work presents an ASV simulator and pipeline that allows training reinforcement learning policies even when starting with unknown vehicle dynamics. It uses a CAD model and limited field trajectories to refine hydrodynamic and thruster parameters through system identification (SysID) using CMAES. This enables zero-shot transfer of RL policies trained in simulation to real-world path following and station-keeping tasks.