NeuroCommitSSM: Decision-Centric Shared Autonomy for Safe Assistive Manipulation via EEG-EMG-ET Commit Readiness
NeuroCommitSSM is a decision-centric framework for safe commit-to-execute control in assistive robotic manipulation. It predicts a continuous commit-readiness score from synchronized EEG, EMG, and eye-tracking, and converts it into discrete commit events via dwell and hysteresis filtering. A three-state finite-state supervisor, HOLD-ASSIST-COMMIT (HAC), gates execution by requiring both sustained commit-readiness from the neural model and real-time feasibility checks. Evaluated on 32 subjects performing five ADL tasks, NeuroCommitSSM achieves 0.950 action-balanced accuracy with 0.75 false commits per 1000 REST windows, and remains robust under sensor dropout. Hardware-in-the-loop validation demonstrates reduced false starts and decision instability without sacrificing task success.
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[Submitted on 16 Jul 2026]
Title:NeuroCommitSSM: Decision-Centric Shared Autonomy for Safe Assistive Manipulation via EEG-EMG-ET Commit Readiness
View a PDF of the paper titled NeuroCommitSSM: Decision-Centric Shared Autonomy for Safe Assistive Manipulation via EEG-EMG-ET Commit Readiness, by Tipu Sultan and 6 other authors
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Abstract: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 this https URL.
Comments: Accepted at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026). 8 pages, 3 figures, and 8 tables
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.15395 [cs.RO]
(or arXiv:2607.15395v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.15395
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tipu Sultan [view email] [v1] Thu, 16 Jul 2026 18:49:44 UTC (4,561 KB)
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