Qwen Vision Language Model

Qwen VL is a multimodal vision-language model series developed by Alibaba’s Qwen team, designed to process images, documents, videos, and text together for understanding and reasoning.[1][2][3]

Qwen VL Overview

Qwen-VL handles visual inputs like photos, screenshots, charts, and PDFs alongside text to perform tasks such as description generation, Q&A, captioning, and object grounding.[3][4]
It combines a Vision Transformer (ViT) encoder on top of the Qwen LLM base, converting visual features into tokens fed into the language model.[5][6]

Key Features

  • Image/Document Understanding: Recognizes text in slides, tables, code snippets, scanned documents (multi-language OCR), and analyzes structure and semantics.[4][1]
  • Spatial/Object Reasoning: Supports bounding box input/output for location-based queries like “Where is this person?” or “Specify coordinates of this button.”[2][5]
  • Video Understanding: Latest Qwen3-VL processes long videos (tens of minutes to over 2 hours) with temporal grounding for scene description, event detection, and summarization.[1][2]

Model Versions and Specs

The series evolved from Qwen-VL (1st gen) to Qwen2-VL and Qwen3-VL, improving context length, visual recognition, and video capabilities.[6][7][8]
Qwen3-VL offers sizes like 4B, 8B, 30B-A3B, and flagship 235B-A22B parameters in Dense/MoE architectures, with Instruct/Thinking variants.[7][2][1]

Technical Highlights

  • Long Context: Native 256K tokens, extendable to 1M+ via Rope scaling (e.g., YaRN), ideal for hundreds of document pages or ultra-long videos.[8][2][1]
  • Multi-Language OCR/Text: Supports 30+ languages for OCR and 100+ for text, enabling multilingual document/UI/sign recognition.[3][7][1]
  • Dynamic Resolution/Multi-Layer Injection: Adjusts image resolution dynamically and injects multi-layer ViT features into LLM layers for detailed and semantic retention.[2][5]

Use Cases

  • Document Intelligence: Extracts structure/fields from scanned contracts, research PDFs, manuals, blueprints; enables Q&A and auto-summarization.[9][4][1]
  • GUI Agents: Identifies buttons/inputs in PC/mobile screens for real-world automation like “Open this app, click menu, upload file.”[1][2]
  • Scientific/Engineering Images: Analyzes graphs, diagrams, medical scans; integrates with code/math for insights.[3][1]

출처
[1] Qwen3-VL: 고급 비전을 갖춘 오픈소스 멀티모달 AI https://docs.kanaries.net/ko/articles/qwen3-vl
[2] Qwen3-VL: Alibaba Qwen팀이 공개한 더 선명한 시각, 더 … https://discuss.pytorch.kr/t/qwen3-vl-alibaba-qwen-multimodal-llm/7867
[3] Qwen-VL: A Versatile Vision-Language Model … https://alphaxiv.org/ko/overview/2308.12966v3
[4] Qwen-VL: 단순 이미지 인식을 넘어 문서 분석까지, 차세대 AI … https://observability.tistory.com/80
[5] [논문 리뷰] Qwen-VL: A Versatile Vision-Language Model … https://velog.io/@lhj/Qwen-VL-A-Versatile-Vision-Language-Model-for-Understanding-Localization-Text-Reading-and-Beyond
[6] Qwen2-VL: 👁️알리바바의 오픈소스 비전 언어모델 https://fornewchallenge.tistory.com/entry/Qwen2-VL-%F0%9F%91%81%EF%B8%8F%EC%95%8C%EB%A6%AC%EB%B0%94%EB%B0%94%EC%9D%98-%EC%98%A4%ED%94%88%EC%86%8C%EC%8A%A4-%EB%B9%84%EC%A0%84-%EC%96%B8%EC%96%B4%EB%AA%A8%EB%8D%B8
[7] Qwen https://namu.wiki/w/Qwen
[8] [논문 리뷰] Qwen3-VL Technical Report https://www.themoonlight.io/ko/review/qwen3-vl-technical-report
[9] Qwen과 Model Studio로 멀티모달 서비스 구축하기 https://www.alibabacloud.com/blog/qwen%EA%B3%BC-model-studio%EB%A1%9C-%EB%A9%80%ED%8B%B0%EB%AA%A8%EB%8B%AC-%EC%84%9C%EB%B9%84%EC%8A%A4-%EA%B5%AC%EC%B6%95%ED%95%98%EA%B8%B0_601179

Nvidia vs. Google for VLA

NVIDIA vs. Google for VLA (Vision-Language-Action) Development

NVIDIA’s approach is platform-first and “simulation-to-real.” Their VLA story is tightly coupled to the Isaac robotics stack and Omniverse/Isaac Lab workflows: generate and curate training data (including synthetic data), train or post-train open robot foundation models like Isaac GR00T (N1 / N1.6), validate in physics-rich simulation, then deploy to edge hardware. NVIDIA positions GR00T as an open, customizable humanoid-focused foundation model with a dual-system design (a slower vision-language reasoning module plus a fast action module) and ships supporting infrastructure such as physics engines and deployment tooling (e.g., NIM microservices). (NVIDIA Newsroom)

Google’s approach is model-first and “reasoning-to-action.” Google DeepMind is building VLA capability on top of the Gemini family, emphasizing strong multimodal reasoning, planning, and generalization for real-world tasks via Gemini Robotics and Gemini Robotics-ER. A notable differentiator is the push for on-device VLA, where Gemini Robotics On-Device is optimized to run locally on robots for lower latency and offline operation—alongside an SDK (initially for trusted testers) to evaluate and fine-tune. (Google DeepMind)

Practical takeaway

  • Choose NVIDIA when you want an end-to-end robotics developer platform (simulation, data pipelines, open models, deployment stack) and you plan to iterate heavily on embodiment and environments. (NVIDIA Developer)
  • Choose Google when your priority is state-of-the-art multimodal reasoning + agentic behavior, especially with a clear path to on-device execution within the Gemini ecosystem. (Google DeepMind)
  • Many teams will end up combining them in practice: NVIDIA-style simulation/data flywheels for scalable training + Gemini-style reasoning models (where available) for higher-level task planning.

Digital Twin Framework

Lucas Systems is building a digital-twin framework that represents real buildings in a persistent, interactive virtual world. Our goal is to keep a living, continuously updated “mirror” of each facility—capturing geometry, assets, and operational context—so teams can monitor conditions, simulate scenarios, and make faster, evidence-based decisions across maintenance, safety, quality, and automation.

To enable this, we are developing on NVIDIA Omniverse as our core platform for real-time 3D collaboration and simulation. By leveraging Omniverse’s USD-based scene representation, physics-aware simulation, and scalable rendering, we can unify BIM/CAD models, reality capture, and sensor data into a single spatial context. This foundation allows our AI systems to understand where events happen in the building, validate changes against ground truth, and generate actionable insights tied to exact locations and assets.

With Omniverse at the center of our stack, Lucas Systems is creating a framework where building data becomes more than records—it becomes a navigable, computable environment. The result is a digital twin that supports remote inspection, risk forecasting, progress and quality verification, and future-ready robotics integration, enabling buildings to be managed as dynamic systems rather than static structures.

Latest VLM Technology

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Latest Vision-Language Models (VLMs)
VLMs are evolving from “describe what you see” into multimodal reasoning systems that can ground language to pixels, parse documents, and understand long videos—unlocking new capabilities for robotics, inspection, and automation. arXiv+1

Primary CTA: Explore Robotics AI
Secondary CTA: Request a Demo

What’s New in VLMs (2024–2026)

1) Long-video understanding is now practical

Modern VLMs can analyze hour-plus videos, retrieve the relevant moments, and summarize events—turning continuous site footage into searchable intelligence. arXiv+1

2) Grounding is becoming a default capability

Instead of only generating text, many models can output bounding boxes, points, and structured coordinates (often as JSON) for visual localization—critical for inspection, mapping, and robotics perception. Hugging Face+1

3) Strong open models are catching up

Open releases like InternVL2.5 report competitive benchmark performance (e.g., MMMU) while offering multiple sizes for deployment and fine-tuning. GitHub+1

4) VLM → VLA (Vision-Language-Action) for robotics

The frontier is Vision-Language-Action (VLA) models that turn images + instructions into actions. OpenVLA trains on large-scale real robot demonstrations, enabling broad manipulation skills with adaptation via fine-tuning. arXiv+1

5) On-device robotics intelligence is accelerating

Robotics VLA systems are being optimized to run locally on robots to reduce latency and improve reliability in connectivity-limited environments. The Verge+1

Key Capabilities You Can Expect

  • Visual Grounding: “Find the crack near the beam” → returns location coordinates and evidence. Hugging Face
  • Document & Drawing Understanding: OCR + layout parsing for forms, checklists, and technical documents. arXiv+1
  • Long-Context Video Reasoning: Detect events, summarize progress, and pinpoint time ranges. arXiv+1
  • Spatial Reasoning Improvements: New datasets and benchmarks target deeper spatial understanding. OpenReview

Models to Know

  • Qwen2.5-VL: Emphasis on localization, document parsing, and long-video comprehension. arXiv+1
  • InternVL2.5: Open multimodal series highlighting strong benchmark performance and scalable sizes. GitHub+1
  • OpenVLA: Open-source VLA trained on large real-world robot demonstration datasets. arXiv+1
  • Gemini Robotics: VLA family designed for robot control, with ongoing work toward practical deployment. arXiv+1

Why This Matters for Construction Robotics

VLMs (and VLAs) can turn unstructured visual data from sites and buildings into reliable, repeatable decisions:

  • Maintenance: Early anomaly detection + evidence-rich reports
  • Safety: Continuous risk scanning and audit trails
  • Quality: Progress/defect verification with grounded references
  • Automation: From “understand” to “act” via VLA policies arXiv+1

About Lucas Systems

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Building Intelligence. Field-Ready Robotics.
Lucas Systems builds AI-powered robots for the construction industry—helping teams maintain buildings, improve safety, raise quality, and automate repetitive work from site to facility operations.

Primary CTA: Talk to an Expert
Secondary CTA: Explore Solutions

Who We Are

Lucas Systems is a robotics AI company focused on the built environment. We combine computer vision, multimodal AI, and rugged hardware to deliver robots that operate where construction and facility teams work—job sites, corridors, rooftops, mechanical rooms, and high-risk zones.

Our mission is simple: make buildings safer and more efficient to build and operate.

Our Mission

To accelerate safer construction and smarter building operations by turning on-site reality into actionable intelligence—automatically, consistently, and at scale.

Our Vision

A world where every building is continuously understood: risks are predicted, defects are caught early, maintenance is proactive, and humans are freed to do higher-value work.

What We Build

AI Robots for the Full Building Lifecycle

  • Maintenance Robotics: Automated inspections for MEP spaces, roofs, façades, and interiors—capturing visual evidence and generating structured reports.
  • Safety Robotics: Continuous hazard scanning (PPE, restricted zones, obstacles, unsafe access), with real-time alerts and audit trails.
  • Quality Robotics: Repeatable progress and workmanship verification—detecting deviations, missing installs, and defects with annotated evidence.
  • Automation Robotics: Task robots that assist with repetitive workflows—documentation, tagging, checklists, and routine patrols.

How It Works

  1. Capture: Robots collect video, images, depth, and sensor signals during autonomous or guided runs.
  2. Understand: AI models interpret scenes, recognize assets, and detect anomalies in context.
  3. Decide: Workflows prioritize issues by severity, location, and operational impact.
  4. Act: Output is delivered as tickets, checklists, dashboards, and integrations with your existing tools.

Why Lucas Systems

  • Built for Real Sites: Robust navigation and sensing in messy, dynamic environments.
  • Actionable Outputs: Not just data—clear findings, evidence, and next steps.
  • Measurable ROI: Reduced rework, fewer incidents, faster closeout, lower downtime.
  • Open Integration Mindset: Designed to connect with CMMS, EHS, QA/QC, and digital twin platforms.

Our Principles

  • Safety First: Every decision should reduce risk for people on site.
  • Trust Through Evidence: Transparent findings with verifiable visual proof.
  • Human-Centered Automation: Robots augment crews; they don’t replace craftsmanship.
  • Continuous Learning: Models improve from feedback, new projects, and changing conditions.

Industries We Serve

  • Commercial and high-rise construction
  • Industrial plants and energy facilities
  • Hospitals and mission-critical buildings
  • Infrastructure facilities and public assets
  • Property and facility management teams

Call to Action

Ready to modernize building maintenance, safety, quality, and automation with robotics AI?

Let’s talk.

  • Request a demo
  • Discuss a pilot project
  • Explore integrations

Robotics AI

Vision, Language and Action can be next stage of your concern.

Modern vision-language models (VLMs) now go beyond captioning: they ground words in pixels, return boxes or points, parse documents, and track events across hour-long videos. Strong open releases such as Qwen2.5-VL and InternVL2.5 are narrowing the gap with closed models, making multimodal stacks easier to deploy and fine-tune. In robotics, VLMs are becoming vision-language-action (VLA) systems: the model interprets instructions, reasons about 3D space, and outputs actions for manipulation. Examples include OpenVLA and Google’s Gemini Robotics, adding safety checks and on-device inference to cut latency in real environments. These models let robots learn from demonstrations and generalize to unseen objects.