7 papers
Self-Supervised Evolutionary Learning of Neurodynamic Progression and Identity Manifolds from EEG During Safety-Critical Decision Making
Xiaoshan Zhou, Carol C. Menassa, Vineet R. Kamat
Human-vehicle interaction in safety-critical traffic environments increasingly incorporates neural sensing to infer user intent and cognitive state, yet most existing approaches ei…
Feasibility of Embodied Dynamics Based Bayesian Learning for Continuous Pursuit Motion Control of Assistive Mobile Robots in the Built Environment
Xiaoshan Zhou, Carol C. Menassa, Vineet R. Kamat
Non-invasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) offer an intuitive means for individuals with severe motor impairments to independently operate ass…
Biologically Inspired Predictive Coding TCN-Transformer for Anticipatory Human-Robot Interaction in Shared Physical Spaces
Xiaoshan Zhou, Carol C. Menassa, Vineet R. Kamat
As mobile robots increasingly operate in environments shared with humans, proactively anticipating human motion rather than responding reactively is critical for preempting collisi…
Retrieval-augmented in-context learning for multimodal large language models in disease classification
Zaifu Zhan, Shuang Zhou, Xiaoshan Zhou +6
Objectives: We aim to dynamically retrieve informative demonstrations, enhancing in-context learning in multimodal large language models (MLLMs) for disease classification. Methods…
Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving
Xiaoshan Zhou, Carol C. Menassa, Vineet R. Kamat
Robots with wheeled, quadrupedal, or humanoid forms are increasingly integrated into built environments. However, unlike human social learning, they lack a critical pathway for int…
Interoceptive Robots for Convergent Shared Control in Collaborative Construction Work
Xiaoshan Zhou, Carol C. Menassa, Vineet R. Kamat
Building autonomous mobile robots (AMRs) with optimized efficiency and adaptive capabilities-able to respond to changing task demands and dynamic environments-is a strongly desired…