用神经信号控制机器人时，实时保障安全与可信性。
Gated Uncertainty-Aware Runtime Dual Invariants for Neural Signal-Controlled Robotics
- 通过双层运行时监控，融合脑信号解码与符号目标定位
- 在低准确率（27-46%）下仍保持94-97%安全率
- 适合高可靠性需求的神经接口机器人系统
直接从神经信号解码用户意图的安全关键辅助系统需严格保证可靠性和可信度。我们提出 GUARDIAN（Gated Uncertainty-Aware Runtime Dual Invariants），一种用于神经信号控制机器人的实时神经符号验证框架。该框架通过校准置信度的脑信号解码、符号化目标定位与双层运行时监控，同时保障逻辑安全与生理可信。在包含9名受试者、5184次试验的BNCI2014运动想象脑电图（EEG）数据集上，即使使用轻量解码器（测试准确率27-46%，ECE置信度偏差0.22-0.41），系统仍实现94-97%的高安全率。模拟噪声测试中，正确干预次数比基线提升1.7倍。监测器运行频率达100Hz，决策延迟低于毫秒级，适用于闭环神经信号系统。21组消融实验表明，系统对信号退化具有渐进式响应，并可生成从意图到计划再到动作的可审计追踪，实现神经证据与可验证机器人行为的关联。
原文摘要 · Abstract (English)
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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