Hybrid Cross-domain Robust Reinforcement Learning

The paper introduces HYDRO, a novel Hybrid Cross-Domain Robust RL framework that combines limited offline data with online simulation to improve policy robustness amid environment uncertainties. HYDRO employs uncertainty filtering and prioritized sampling to minimize performance gaps between simulators and real-world models, demonstrating superior sample efficiency and performance across various tasks.