Gated Uncertainty-Aware Runtime Dual Invariants for Neural Signal-Controlled Robotics

• URL: http://arxiv.org/abs/2511.20570v1
• Date: Tue, 25 Nov 2025 18:05:05 GMT
• Title: Gated Uncertainty-Aware Runtime Dual Invariants for Neural Signal-Controlled Robotics
• Abstract summary: GUARDIAN is a framework for real-time neuro-symbolic verification for neural signal-controlled robotics.<n>GUARDIAN enforces both logical safety and physiological trust by coupling confidence-calibrated brain signal decoding with symbolic goal grounding.
• Score: 1.8381910913370485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safety-critical assistive systems that directly decode user intent from neural signals require rigorous guarantees of reliability and trust. We present GUARDIAN (Gated Uncertainty-Aware Runtime Dual Invariants), a framework for real-time neuro-symbolic verification for neural signal-controlled robotics. GUARDIAN enforces both logical safety and physiological trust by coupling confidence-calibrated brain signal decoding with symbolic goal grounding and dual-layer runtime monitoring. On the BNCI2014 motor imagery electroencephalogram (EEG) dataset with 9 subjects and 5,184 trials, the system performs at a high safety rate of 94-97% even with lightweight decoder architectures with low test accuracies (27-46%) and high ECE confidence miscalibration (0.22-0.41). We demonstrate 1.7x correct interventions in simulated noise testing versus at baseline. The monitor operates at 100Hz and sub-millisecond decision latency, making it practically viable for closed-loop neural signal-based systems. Across 21 ablation results, GUARDIAN exhibits a graduated response to signal degradation, and produces auditable traces from intent, plan to action, helping to link neural evidence to verifiable robot action.

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