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

• URL: http://arxiv.org/abs/2604.09474v1
• Date: Fri, 10 Apr 2026 16:33:32 GMT
• Title: SafeMind: A Risk-Aware Differentiable Control Framework for Adaptive and Safe Quadruped Locomotion
• Abstract summary: We introduce SafeMind, a differentiable safety-control framework for learning-based quadruped controllers.<n>SafeMind explicitly models and aleatoric uncertainty through a variance-aware barrier constraint embedded in a differentiable program.<n>We 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.
• Score: 19.50816075941739
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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