<ul><li>Ensuring safety in reinforcement learning (RL)-based robotic systems is a critical challenge, especially in contact-rich tasks within unstructured environments.</li><li>Current safe RL approaches focus on high-level recovery mechanisms, neglecting low-level execution safety.</li><li>The proposed method, Bresa, decouples task learning from safety learning, incorporating a safety critic network that operates at a higher frequency for real-time intervention in unsafe conditions.</li><li>Bresa outperforms the baseline in multiple tasks and provides a reflexive safety mechanism that bridges the gap between planning and execution.</li></ul>

Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks

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