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CASE STUDIES / ROBOTICS IN PRACTICE

Real robots.
Specific work.

Explore how robots are being used in factories, warehouses, schools and research. See the task, the evidence and what made each project work.

Evidence reviewed 22 September 2026

AGIBOT G2 manufacturer product image; not a photograph of the Longcheer installation
AGIBOT G2 · product image
Inside the Longcheer case
07brand case studies
06brands explored
03types of evidence

Independent editorial summaries of third-party projects. These are not ROBODROMO installations or endorsements. Reported results belong to the specific configuration and conditions described.

01 / AGIBOTProduction use reported
AGIBOT G2 manufacturer product image; not a photograph of the Longcheer installation
Manufacturer product image · not the installation

AGIBOT G2

Tablet testing, on a working production line.

Longcheer · Nanchang, China

G2 robots load and unload tablet test stations, then sort the tested devices. Longcheer confirms the move into its production lines.

310tablets/hour · up to, reported by AGIBOT

AGIBOT, 15 April 2026 · customer follow-up, 24 April 2026

Read the evidence
Reported result
AGIBOT reports up to 310 units per hour, over 99.9% task success and more than 140 cumulative operating hours. The release also reports 36-hour line integration after a four-month development programme. These are separate measures, not a single timed trial.
What the project needed
The customer contributed its production process and test equipment. AGIBOT supplied G2 robots and task software; the teams developed the integration together.
What this does not establish
Manufacturer-reported figures, not an independent audit. Public raw logs, exact intervention rates and full project cost are unavailable. A standard G2 order does not include this completed workstation.

For your project

Plan the test fixtures, equipment interfaces and exception handling alongside the robot.

  1. Pick tablet
  2. Load test fixture
  3. Sort by result

Original sources

02 / UnitreeResearch experiments
Unitree G1 manufacturer product image; research configuration may differ
Manufacturer product image · not the installation

Unitree Go2 + G1

Finding objects across unfamiliar rooms.

Carnegie Mellon University · USA

CMU describes an object-navigation system tested on Go2, G1 and a custom wheeled robot. The software separates reasoning, route planning and motion control.

190real-world experiments · across three robot platforms

CMU thesis presentation · 11 September 2026

Read the evidence
Reported result
The university reports 190 real-world experiments across the system and navigation that was four to five times more efficient than its research baselines. The abstract does not give a separate success rate or trial count for each robot.
What the project needed
Researchers supplied the navigation and vision-language software. Robot-specific movement control sits beneath shared planning and reasoning.
What this does not establish
Research results, not an installed warehouse service or a standard Unitree feature. The comparison is with research baselines, not human workers. Edition, additional hardware and commercial support require confirmation.

For your project

Choose development access and a technical team around the task you want to build.

  1. Identify target
  2. Plan across rooms
  3. Move to the object

Original sources

03 / UnitreeUniversity integration
Unitree Go2 manufacturer product image; Southampton added its own camera integration
Manufacturer product image · not the installation

Unitree Go2

A Go2 becomes a research platform.

University of Southampton · UK

Southampton documents its own Go2 setup, including ROS 2 control and a successfully integrated Intel RealSense D435i depth camera.

ROS 2documented control and camera integration

University operating guide · undated; checked 22 September 2026

Read the evidence
Reported result
The guide identifies a working camera integration and both Wi-Fi and Ethernet control routes. It also records that a separate Livox MID-360 integration was unfinished when documented.
What the project needed
The university configured its robot around ROS 2 Foxy and published operating instructions for researchers. This is evidence of practical use and integration work.
What this does not establish
No commercial inspection results, operating-hour total or cost saving is reported. This historical software setup is not a recommendation to adopt the same version today, or proof of compatibility with every Go2 edition.

For your project

Budget for setup, access, compatible sensors and someone who owns the software configuration.

  1. Connect robot
  2. Integrate sensors
  3. Run research software

Original sources

04 / JDWarehouse operation reported
  1. Bring goods to people
  2. Pick and consolidate
  3. Dispatch orders

JD Logistics warehouse system

Goods-to-person fulfilment in Europe.

Hunkemöller · Venray, Netherlands

JD describes a Dutch fulfilment operation serving Hunkemöller, using mobile robots, robotic arms, storage and warehouse software together.

12,000m² warehouse · reported by JD

JD Logistics · 10 November 2021; follow-up 8 April 2022

Read the evidence
Reported result
JD reported a 12,000 m² warehouse and estimated that average daily order production time had fallen by two-thirds. Its 2022 account confirms goods-to-person AGVs supporting business and consumer orders.
What the project needed
JD provided the warehouse operation and integrated automation. Hunkemöller supplied the retail fulfilment requirement. The result concerns the complete facility.
What this does not establish
A historical supplier report, not a 2026 operating audit. The estimated time saving is not an independently verified productivity or headcount saving. The sources do not identify today’s Auto Sorting Wall or Tote Handling Solution as the installed models.

For your project

Start with order profiles, layout and the required workflow before choosing individual machines.

  1. Bring goods to people
  2. Pick and consolidate
  3. Dispatch orders

Original sources

05 / DOBOTProduction use reported
  1. Handle the glass
  2. Rotate separately
  3. Feed decoration process

DOBOT CR10A / two-cobot cell

Two cobots share a glass-handling task.

SAHM Glasveredelung · Germany

Integrator MS Electronics redesigned SAHM’s glass-decoration handling around two cobots. A CR10A handles the glass; a second cobot performs rotation.

2cobots · handling and rotation split between them

DOBOT customer case · undated; checked 22 September 2026

Read the evidence
Reported result
DOBOT says the revised cell met the required cycle time and completed site acceptance and commissioning within a few days. It does not publish the cycle time, reject rate or payback.
What the project needed
MS Electronics designed and integrated the cell. The project included factory acceptance testing before installation at SAHM’s production site.
What this does not establish
This is an industrial-arm case, not evidence that the LUMO humanoid can perform the task. The second robot’s exact model is not identified in the account. Numerical productivity gains are not supplied.

For your project

Splitting a task between specialised machines may work better than asking one robot to do everything.

  1. Handle the glass
  2. Rotate separately
  3. Feed decoration process

Original sources

06 / MikoSchool use reported
Miko 3 manufacturer product image; not a photograph of pupils or the school
Manufacturer product image · not the installation

Miko 3

Miko 3 joins the learning routine.

Primary School Savremena · Serbia

Savremena describes Miko 3 as part of its educational robot programme, with conversation, quizzes and learning activities alongside other classroom tools.

Miko 3named by the school in its teaching approach

School account · undated; checked 22 September 2026

Read the evidence
Reported result
The school’s own account identifies the robot and how it is intended to support pupils. It does not publish a controlled learning study, attainment results or a verified number of robots in use.
What the project needed
Teachers and the school provide the learning context. The school describes Wi-Fi-connected interaction and links to its teaching materials.
What this does not establish
School-reported adoption, not proof of improved intelligence, wellbeing or exam results. Any local curriculum integration must be checked separately; it is not a standard feature promised with a UK Miko 3 purchase.

For your project

Choose a supervised learning activity first, then check content, connectivity and the school’s data requirements.

  1. Choose an activity
  2. Interact and practise
  3. Teacher reviews use

Original sources

07 / LUWUPublished real-robot experiments
  1. Interpret in cloud
  2. Validate locally
  3. Move and check

LUWU XGO-Rider

Cloud instructions. Local movement.

Chen and colleagues · Robotics and Autonomous Systems

Researchers tested an XGO-Rider with a cloud language model, local command checks and bounded movement actions. The paper separates the AI service from the robot’s control.

17.47 smean voice-to-feedback delay · 20 interactions

Research article online · 10 September 2026

Read the evidence
Reported result
The abstract reports 17.47 ± 2.72 seconds of interaction latency (20 trials). In a matched 100-scenario comparison, task success was 90%, versus 78% for a detector-and-controller baseline.
What the project needed
The research combines cloud speech and language services, visual detection, a local validator and the manufacturer’s movement SDK.
What this does not establish
Results describe a custom research system. The delay is not the motor-control period. They do not establish commercial uptime, offline capability or superiority to trained robot policies. Evidence checked from the publisher’s abstract, not a reproduced experiment.

For your project

Ask where decisions run and what happens when the network slows down. These details affect the experience as much as the robot body.

  1. Interpret in cloud
  2. Validate locally
  3. Move and check

Original sources

CASE STUDIES / ROBOTICS IN PRACTICE

A good case study answers four questions.

01

What was the task?

A specific job, with a defined start and finish.

02

What was added?

The software, tools, site changes and people behind it.

03

What was measured?

Units, periods and the organisation reporting the result.

04

What is still unknown?

Costs, interventions and conditions that need checking.

WIDER INDUSTRY / EXISTING REFERENCES

More lessons from the field.

Keep the robot and generation in view. These wider examples help explain deployment; they are not products promised by ROBODROMO.

Skild AI × NVIDIA × Foxconn

01 / Skild AI × NVIDIA × Foxconn

DEPLOYMENT RELATADO

Montagem de sistemas Blackwell

NVIDIA · 10 Setembro 2026

Demonstração da Assembleia publicada por NVIDIA. O relatório ligado fornece o contexto do projecto. Assista ao filme de origem ↗
  1. Componentes de posição
  2. Fixar 16 Parafusos
  3. Perturbações do manuseamento
Quem trouxe o quê?
Skild fornece a inteligência do robô; NVIDIA Contribui para a infraestrutura de IA e ferramentas de robótica; Foxconn fornece a configuração de fabricação. O relatório descreve manipuladores de braço duplo sem nomear seu fabricante.
O que eles desenvolveram
Uma sequência que coloca um barramento e bloco de limite, fixa 16 parafusos e responde a perturbações durante a montagem.
O que as provas dizem
NVIDIA relata a colaboração e mostra o fluxo de trabalho de montagem. Skild também descreve a implantação em sua própria conta.
Planeando um projeto semelhante
Para uma instalação semelhante, acordar o tempo de ciclo sustentado, a intervenção humana esperada e o custo operacional total como parte da avaliação do projeto.
A lição para o seu projeto

Um corpo robô capaz precisa de software de tarefa, ferramentas e um processo que pode ser testado em conjunto.

Leia o relatório original ↗
Agility Robótica × GXO

03 / Agility Robótica × GXO

DEPLOIO COMERCIAL

Movendo totes para um fluxo de trabalho existente

GXO · 27 Junho 2024; mais tarde Agility atualizar

Agility O filme de implantação. Veja os relatórios vinculados para o contexto operacional e resultados relatados. Assista ao filme de origem ↗
  1. Receber um tote
  2. Levantamento e transferência
  3. Posicionar no transportador
Quem trouxe o quê?
GXO forneceu o fluxo de trabalho do armazém em uma instalação SPANX. Agility fornecido Digit e a sua plataforma de gestão da frota Arc, sob um acordo Robôs-como-um-Serviço.
O que eles desenvolveram
Transferência total de robôs móveis para transportadores, conectados com a automação existente do site através do Arc.
O que as provas dizem
O acordo comercial seguiu-se a: 2023 Piloto. Agility mais tarde reportado mais do que 100,000 totes movidos em GXO A sede do ramo florido.
Planeando um projeto semelhante
Para o seu armazém, avalie o custo por tote aceito, taxa de intervenção e desempenho contra o fluxo de trabalho existente. Estas medidas não são fornecidas pela contagem cumulativa de portagens.
A lição para o seu projeto

Comece com uma entrega entre os processos existentes e meça como funciona de forma confiável.

Leia o relatório original ↗
Figure × BMW Grupo

04 / Figure × BMW Grupo

PILOTO DE PRODUÇÃO COMPLETO

Posicionamento de peças metálicas para soldadura

BMW Grupo · Março retrospectivo; Junho 2026 acompanhamento

  1. Obter uma parte
  2. Posição no dispositivo
  3. Entrega para soldagem
Quem trouxe o quê?
BMW forneceu o processo de produção e as equipes de plantas. Figure fornecido Figure 02 e trabalhou com BMW no pedido.
O que eles desenvolveram
Recuperando e posicionando componentes de chapa-metal para soldagem na planta de Spartanburg, com mudanças nos arranjos de segurança e conectividade da planta.
O que as provas dizem
BMW relatórios terminados 90,000 componentes manipulados em torno de 1,250 horas de funcionamento durante a sua 2025 Piloto. Estes números descrevem esse piloto, não todos os Figure Robô.
Planeando um projeto semelhante
Este foi um projeto dedicado com BMW e Figure Equipas de engenharia. Sua tarefa precisa de seu próprio equipamento, integração, suporte e avaliação comercial.
O que aconteceu depois?

Em Junho 2026, BMW disse as conclusões de Figure 02 formaram a base para um novo Figure 03 Projecto de logística. O sucessor tem diferentes hardware e capacidades; a aprendizagem continuou através de gerações de robôs.

Ler BMW Junho 2026 atualizar ↗

Um primeiro projeto focado pode construir conhecimento de site e tarefa para o próximo. Essa é uma razão para aprendermos cedo, em vez de garantirmos poupanças futuras.

Leia o relatório original ↗

ROBODROMO / YOUR NEXT STEP

What could a first project prove for you?

Start with one useful task. We can help shape the brief and identify a supplier or technical partner. Agree the equipment, support and success measures before committing to a trial.