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, thereby preserving gradient flow for end-to-end training. A semantics-to-constraint encoder modulates safety margins using 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 terrain types, dynamic obstacles, morphology perturbations, and semantically defined tasks. Experiments show that SafeMind reduces safety violations by 3--10x and energy consumption by 10--15% relative to state-of-the-art CBF, MPC, and hybrid RL baselines, while maintaining real-time control performance.

翻译：基于学习的四足控制器虽能实现惊人的敏捷性，但在模型不确定性、感知噪声及非结构化接触条件下普遍缺乏形式化安全保证。本文提出SafeMind——一种可微分随机安全控制框架，其将概率控制障碍函数与语义上下文理解及元自适应风险校准相统一。SafeMind通过嵌入可微分二次规划中的方差感知障碍约束显式建模认知不确定性与偶然不确定性，从而保持端到端训练的梯度流。其语义-约束编码器利用感知或语言线索调节安全裕度，而元自适应学习器能跨环境持续调整风险敏感度。我们给出了随机动力学下概率正向不变性、可行性及稳定性的理论条件。SafeMind以200赫兹频率部署于Unitree A1和ANYmal C机器人，在12种地形类型、动态障碍物、形态扰动及语义定义任务中完成验证。实验表明：相比当前最先进的CBF、MPC及混合强化学习基线方法，SafeMind将安全违规率降低3-10倍，能耗降低10-15%，同时保持实时控制性能。