Research on Vision-Language Question Answering Models for Industrial Robots

2026-05-02

AI Summary

This work addresses the challenges of visual-language question answering (VLQA) in human–robot interaction scenarios involving industrial robots, where semantic ambiguity, complex scene layouts, and domain-specific terminology hinder accurate understanding. To tackle these issues, the authors propose a hierarchical cross-modal fusion model that integrates region-level visual feature extraction, multi-scale encoding, syntactic parsing, and task-aware semantic attention. The model further incorporates an adaptive fusion strategy and a context-driven gating mechanism to enable fine-grained alignment between visual and linguistic signals within a unified joint reasoning space. Experimental results on the IVQA and RIF benchmarks demonstrate that the proposed approach significantly outperforms existing VLQA methods in Top-1 accuracy, semantic alignment capability, and robustness in interpreting ambiguous or procedural instructions.

Abstract

A hierarchical cross-modal fusion model is proposed for vision-language question answering (VLQA) in industrial robotics, targeting the challenges of semantic ambiguity, complex environmental layouts, and domain-specific terminology common in modern manufacturing. The framework integrates advanced object detection, multi-scale visual encoding, syntactic parsing, and task-aware semantic attention to unite vision and language signals into a joint reasoning space. Region-based deep networks extract visual features, weighted embeddings aggregate, and recurrent neural parsing encodes sentence structures. Through fine-grained semantic alignment driven by adaptive fusion and cross-attention mechanisms, the system can handle operational queries, instruction steps, and anomaly detection with higher reliability. Compared to the existing VLQA benchmarks, validation experiments conducted on the IVQA and RIF benchmarks indicate improvements in semantic alignment, Top-1 accuracy, and robustness to ambiguous or procedural task queries. Ablation studies further quantify the impact of each architectural module, confirming the necessity of multi-level feature integration and context-driven gating for dependable industrial deployment. The technical advancements reported here provide core methodologies to improve the interpretability and operational effectiveness of industrial robots faced with diverse human-robot interaction tasks.

Problem

• vision-language question answering
• industrial robotics
• semantic ambiguity
• domain-specific terminology
• complex environments

Innovation

• hierarchical cross-modal fusion
• vision-language question answering
• adaptive fusion
• task-aware semantic attention
• multi-scale visual encoding