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

Tasha Kim, Oiwi Parker Jones

Keywords: safe and trustworthy AI, runtime verification, BCI-based control, assistive robotics, brain-computer interfaces, human-robot collaboration

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 $\approx$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.

Submission Number: 1