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Deep reinforcement learning has driven remarkable progress in quadrupedal locomotion, yet the resulting controllers typically treat the robot's physical limits as soft penalties in the reward, offering no guarantee that joint limits, leg kinematics, or obstacle clearance are respected during or after training. Safe-exploration methods promise such guarantees, but they have been developed almos
