2025
Learning Robust Policies via Interpretable Hamilton-Jacobi Reachability-Guided Disturbances
ICRA 2025
Deep Reinforcement Learning (RL) has shown remarkable success in robotics with complex and heterogeneous dynamics. However, its vulnerability to unknown disturbances and adversarial attacks remains a significant challenge. In this paper, we propose a robust policy training framework that integrates