NeuroCommitSSM：通过脑电图-肌电图-眼动追踪提交准备实现安全辅助操作的以决策为中心的共享自主性
NeuroCommitSSM: Decision-Centric Shared Autonomy for Safe Assistive Manipulation via EEG-EMG-ET Commit Readiness
- Saint Louis University（圣路易斯大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
NeuroCommitSSM是用于辅助机器人操作安全控制的以决策为中心框架，依EEG、EMG和ET预测提交分数转化为离散事件，经三状态监督器控制执行。实验表明其在多场景下准确率高、错误提交低，硬件验证减少错误启动和决策不稳定。
AI中文摘要：
我们提出了NeuroCommitSSM，这是一个以决策为中心的框架，用于在辅助机器人操作中进行安全的提交执行控制，它不仅对做什么进行建模，还对何时执行进行建模。NeuroCommitSSM根据同步的脑电图（EEG）、肌电图（EMG）和眼动追踪（ET）预测[0,1]范围内的连续提交准备分数c_t，并通过驻留和滞后滤波将其转换为离散的提交事件。一个三状态有限状态监督器HOLD-ASSIST-COMMIT（HAC），在启动运动之前，通过要求神经模型持续的提交准备信号以及实时感知和机器人状态可行性（包括目标可见性、逆运动学可解性和无碰撞规划）来控制执行。我们使用留一法交叉验证和七种传感器丢失场景（S0-S6），对32名受试者进行与国际功能、残疾和健康分类（ICF）一致的五项日常生活（ADL）任务的框架进行了评估。NeuroCommitSSM实现了0.950的动作平衡准确率，每1000个休息窗口有0.75个错误提交事件（FP/1k REST），并在传感器丢失情况下保持低错误提交和稳定的状态转换。例如，在仅脑电图的情况下，它实现了0.785的平衡准确率和0.29 FP/1k REST，而时间卷积网络基线在相同条件下产生99.95 FP/1k REST。在Kinova Gen3手臂上的硬件在环（HIL）验证表明，经过可行性检查的执行减少了错误启动和决策不稳定性，同时不牺牲任务成功率。补充材料，包括代码、数据集、视频和额外分析，可在此https URL获得。
英文摘要：
We present NeuroCommitSSM, a decision-centric framework that models when to execute, not just what to do, for safe commit-to-execute control in assistive robotic manipulation. NeuroCommitSSM predicts a continuous commit-readiness score c_t in [0,1] from synchronized electroencephalography (EEG), electromyography (EMG), and eye-tracking (ET), and converts it into discrete commit events through dwell and hysteresis filtering. A three-state finite-state supervisor, HOLD-ASSIST-COMMIT (HAC), gates execution by requiring both a sustained commit-readiness signal from the neural model and real-time perception and robot-state feasibility, including target visibility, inverse kinematics solvability, and collision-free planning, before initiating motion. We evaluate the framework on N=32 subjects performing five activities of daily living (ADL) tasks aligned with the International Classification of Functioning, Disability and Health (ICF), using leave-one-subject-out (LOSO) cross-validation and seven sensor-dropout scenarios (S0-S6). NeuroCommitSSM achieves 0.950 action-balanced accuracy with 0.75 false commit events per 1000 REST windows (FP/1k REST), and maintains low false commits and stable state transitions under sensor loss. For example, in the EEG-only condition, it achieves 0.785 balanced accuracy and 0.29 FP/1k REST, whereas the Temporal Convolutional Network baseline produces 99.95 FP/1k REST under the same condition. Hardware-in-the-loop (HIL) validation on a Kinova Gen3 arm shows that feasibility-checked execution reduces false starts and decision instability without sacrificing task success. Supplementary materials, including code, datasets, videos, and additional analyses, are available at https://madibabaiasl.github.io/NeuroCommitSSM/.
