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Passivity-Centric Safe Reinforcement Learning for Contact-Rich Robotic Tasks
Reinforcement learning (RL) has achieved remarkable success in various robotic tasks; however, its deployment in real-world scenarios, particularly in contact-rich environments, often overlooks critical safety and stability aspects. Policies without passivity guarantees can result in system instabil
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