Meta-learning enhanced human–robot collaboration in visual communication design instruction
Year: 2026
Type: article
Abstract: Visual icon-based human–robot interaction faces challenges of low efficiency and asynchronous responses in educational settings. This study proposes the Adaptive Graph-based Visual Teaching and Robot Interaction (AGVTRI) model for optimizing robot teaching networks. Integrating a Vision Transformer, graph neural network, and meta-learning, the model achieves adaptive visual feature extraction and relationship reasoning. Validated on Icons-50 and SVG datasets, it reaches 0.869 classification accuracy and 0.961 top-K accuracy. Physical tests on four educational robots show reduced interaction latency and improved action accuracy. The AGVTRI model enables high-fidelity mapping between visual cues and robot actions, supporting efficient, scalable intelligent teaching systems.
Source: Scientific Reports
Author: Xiaxia Feng
Institution: Xi'an University of Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7212229546
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