The research objective of this project is to apply fundamental theories at the intersection of artificial intelligence, deep learning, human-robot interaction, and multi-modal signal processing to develop new solutions for human intent recognition and understanding for robots. This approach aims to bridge the gap between user intent and device action to enhance the interaction between humans and smart technologies, such as mobile manipulators.
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- NeuroCommitSSM: Decision-Centric Shared Autonomy for Safe Assistive Manipulation via EEG-EMG-ET Commit Readiness, IROS 2026
- Paper’s Repository (Code & Implementation): https://github.com/madibabaiasl/NeuroCommitSSM
- Paper’s Website: https://madibabaiasl.github.io/NeuroCommitSSM/
- TriSaFe-Trans: A Safety-Aware Multimodal Intent Recognition Pipeline for Assistive Robotics, BioRob 2026
- Paper’s Repository (Code & Implementation): https://github.com/madibabaiasl/SafeIntentDetectionPaper/wiki, 2026
- NeuroFusion-Trans: A Novel Transformer-Based EEG-EMG Fusion Model for Assistive Robotics, 2025 IEEE Access
- Paper’s Repository (Code & Implementation): https://github.com/madibabaiasl/NeuroFusion-Trans, 2025.
- TransNN-MHA: A Transformer-Based Model to Distinguish Real and Imaginary Motor Intent for Assistive Robotics, 2025 IEEE Access
- Papers’s Repository (Code & Implementation): http://github.com/madibabaiasl/EEGIntentPaper, 2025.
- EMG-TransNN-MHA: A Transformer-Based Model for Enhanced Motor Intent Recognition in Assistive Robotics, 2024 IEEE International Conference on Big Data (BigData)
- Paper’s Repository (Code & Implementation): https://github.com/madibabaiasl/EMGIntentPaper, 2024
