[2604.09474] SafeMind: A Risk-Aware Differentiable Control Framework for Adaptive and Safe Quadruped Locomotion

Computer Science > Robotics arXiv:2604.09474 (cs) [Submitted on 10 Apr 2026] Title:SafeMind: A Risk-Aware Differentiable Control Framework for Adaptive and Safe Quadruped Locomotion Authors:Zukun Zhang, Kai Shu, Mingqiao Mo View a PDF of the paper titled SafeMind: A Risk-Aware Differentiable Control Framework for Adaptive and Safe Quadruped Locomotion, by Zukun Zhang and 2 other authors View PDF HTML (experimental) Abstract: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...