SafeMind: A Risk-Aware Differentiable Control Framework for Adaptive and Safe Quadruped Locomotion

Learning-based quadruped controllers achieve impressive agility but typically lack formal safety guarantees under model uncertainty, perception noise, and unstructured contact conditions. We introduce SafeMind, a differentiable stochastic safety-control framework that unifies probabilistic Control Barrier Functions with semantic context understanding and meta-adaptive risk calibration. SafeMind explicitly models epistemic and aleatoric uncertainty through a variance-aware barrier constraint embedded in a differentiable quadratic program, preserving gradient flow for end-to-end training. A semantics-to-constraint encoder modulates safety margins from perceptual or language cues, while a meta-adaptive learner continuously adjusts risk sensitivity across environments.We provide theoretical conditions for probabilistic forward invariance, feasibility, and stability under stochastic dynamics. SafeMind is deployed on Unitree A1 and ANYmal C at 200 Hz and validated across 12 terrains, dynamic obstacles, morphology perturbations, and semantically defined tasks. Experiments show that SafeMind reduces safety violations by 3–10× and energy consumption by 10–15% compared to state-of-the-art CBF, MPC, and hybrid RL baselines, while maintaining real-time control performance.