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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 배송

조립 Blackwell 시스템

NVIDIA · 10 9월 9일 2026

회의 데모에 의해 게시 됨 NVIDIA. 연결된 보고서는 프로젝트 컨텍스트를 제공합니다. 소스 필름보기 ↗
  1. 위치 구성
  2. 패스워드 16 스크랩
  3. 손잡이 방해
누가 무슨 일이 있었는지
Skild는 로봇 인텔리전스를 제공합니다; NVIDIA AI 인프라 및 로봇 도구에 기여; Foxconn 제조 설정 제공. 보고서는 제조 업체를 naming하지 않고 이중 팔 조작자를 설명합니다.
그들이 개발한 것
busbar 및 limit block을 배치하는 순서, 패스 16 나사 및 조립 중에 방해에 응답합니다.
증거는 말한다
NVIDIA 협업을 보고 어셈블리 워크플로우를 보여줍니다. Skild는 자체 계정에서 배포를 설명합니다.
비슷한 프로젝트 계획
유사한 임명을 위해, 지속된 주기 시간, 예상한 인간적인 개입 및 프로젝트 평가의 부분으로 가득 차있는 운영 비용.
당신의 프로젝트에 대한 교훈

가능한 로봇 몸은 작업 소프트웨어, 도구 및 함께 테스트 할 수있는 프로세스를 필요로합니다.

원본 보고서를 읽으십시오 ↗
Agility 로봇공학 × GXO

03 / Agility 로봇공학 × GXO

광고 DEPLOYMENT

기존 워크플로우로 이동하기

GXO · 27 6월 1일 2024; 이름 * Agility 지원하다

Agility’s 배포 영화. 작동 상황에 대한 링크 된 보고서를 참조하고 결과를보고. 소스 필름보기 ↗
  1. tote를 받기
  2. 상승과 이동
  3. 컨베이어에 장소
누가 무슨 일이 있었는지
GXO SPANX 시설의 창고 작업 흐름을 공급했습니다. Agility 제품 설명 Digit 그리고 Robots-as-a-Service 계약에 따라 Arc 함대 관리 플랫폼.
그들이 개발한 것
모바일 로봇에서 컨베이어로 이동하여 Arc을 통해 현장의 기존 자동화와 연결됩니다.
증거는 말한다
상업적 계약은 다음과 같습니다. 2023 파일 형식 Agility 나중에 더 많은 것을 보고했습니다 100,000 totes로 이동 GXO’s Flowery 지점 시설.
비슷한 프로젝트 계획
창고의 경우, 기존 워크플로우에 대한 허용된 토트, 개입률 및 성능 당 비용을 평가합니다. 이 측정은 cumulative tote 조사에 의해 공급되지 않습니다.
당신의 프로젝트에 대한 교훈

기존 프로세스를 통해 한 번의 핸드오버를 시작하고, 안정적으로 작동하는 방법을 측정합니다.

원본 보고서를 읽으십시오 ↗
Figure × BMW 이름 *

04 / Figure × BMW 이름 *

완료된 생산 PILOT

용접을 위한 포지셔닝 금속 부속

BMW 그룹 · 3 월 복고풍; 6월 1일 2026 관련 기사

  1. 자주 묻는 질문
  2. 정착물에 있는 위치
  3. 용접에 대 한 손
누가 무슨 일이 있었는지
BMW 생산 공정 및 플랜트 팀 공급. Figure 제품 설명 Figure 02 그리고 함께 일 BMW 신청에.
그들이 개발한 것
Spartanburg 플랜트에서 용접을 위한 시트 금속 부품, 안전 배열 및 플랜트 연결에 대한 변경 사항이 있습니다.
증거는 말한다
BMW 더 보기 90,000 부품 처리에 대해 1,250 영업 시간 2025 파일 형식 이 그림은 파일럿이 아닌 모든 것을 설명합니다. Figure 로봇.
비슷한 프로젝트 계획
이것은 전용 프로젝트였습니다. BMW 및 Figure 엔지니어링 팀. 작업은 자체 장비, 통합, 지원 및 상업 평가를 필요로 합니다.
다음 것

6월 2026, BMW 에서 발견 말했다. Figure 02 새로운 기준 형성 Figure 03 물류 프로젝트. 승계에는 다른 기계설비 및 기능이 있습니다; 학습은 로봇 세대에 걸쳐 계속됩니다.

읽음 BMW 6월 2026 지원하다 ↗

집중된 첫 번째 프로젝트는 사이트와 작업 지식을 다음 단계로 구축할 수 있습니다. 그것은 초기 학습하는 이유, 오히려 미래의 저축의 보장보다.

원본 보고서를 읽으십시오 ↗

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.