Research on Vision-Language Question Answering Models for Industrial Robots

TL;DR

This paper introduces a hierarchical cross-modal fusion model for vision-language question answering in industrial robotics, enhancing semantic understanding and operational reliability in complex manufacturing environments.

Contribution

It presents a novel multi-level feature integration framework combining advanced visual encoding, syntactic parsing, and semantic attention for industrial VLQA tasks.

Findings

Improved semantic alignment and Top-1 accuracy on IVQA and RIF benchmarks.

Enhanced robustness to ambiguous and procedural queries.

Ablation studies confirm the importance of multi-level feature fusion.

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.