<\/strong><\/h2>\n\n\n\nProject Background<\/strong><\/strong><\/h2>\n\n\n\nTogether with Pollen Robotics and Seeed Studio, <\/strong>Reachy Mini is the first open-source desktop robot introduced by Hugging Face<\/a><\/strong>, built to make experimenting with robotics and AI more accessible. Designed to be expressive, programmable, and easy to interact with, it allows users to explore human\u2013robot interaction and intelligent behavior right from their desk.<\/p>\n\n\n\nMore than just a robot product, Reachy Mini functions as a development platform that encourages hands-on exploration and community collaboration. It provides an approachable way for people to prototype ideas, learn about robotics, and build interactive AI experiences.<\/p>\n\n\n\n
As the production of Reachy Mini scaled up to over 3,000 pcs, one key step in the assembly process required operators to visually confirm:<\/p>\n\n\n\n
\n- Whether the servo motor orientation<\/strong> was correct<\/li>\n<\/ul>\n\n\n\n
![\"In](\"https:\/\/wdcdn.qpic.cn\/MTY4ODg1ODA1MzcyMTU3NA_491282_Fsps2rkHHg-Z5ttA_1773112408?w=1702&h=1276&type=image\/jpeg\")
<\/p>\n\n\n\n
(The circular component on the lower layer has two vertical lines on one side and a single vertical line on the other. In the correct assembly orientation, the side with two vertical lines should face outward, while the side with the single vertical line should face inward.)<\/p>\n\n\n\n
\n- Whether the nameplate matched the model number<\/li>\n<\/ul>\n\n\n\n
![\"\"](\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/03\/recamera-reachy-mini-1030x773.jpg\")
(Each model number has a corresponding nameplate, and the mapping between them has been pre-stored in the system. The reCamera will verify whether the model number and nameplate match correctly.)<\/figcaption><\/figure>\n\n\n\nThis verification step occurs during the assembly inspection stage<\/strong>, where each unit must be checked before proceeding to final assembly.<\/p>\n\n\n\nBecause this process relied entirely on human inspection, it introduced several operational risks:<\/p>\n\n\n\n
\n- Human visual inspection can occasionally miss errors<\/li>\n\n\n\n
- The step requires dedicated manual labor time<\/strong><\/li>\n\n\n\n
- Inconsistent inspection quality can lead to downstream rework<\/li>\n<\/ul>\n\n\n\n
To address this, the engineering team began exploring whether an edge AI vision system<\/strong> could automate this inspection step.<\/p>\n\n\n\n<\/p>\n\n\n\n
The Challenge<\/strong><\/strong><\/h2>\n\n\n\nManual Visual Inspection<\/strong><\/strong><\/h3>\n\n\n\nOriginally, this inspection step relied entirely on a human operator who visually checked every servo during the assembly process.<\/p>\n\n\n\n
This process required one dedicated operator<\/strong> and still carried the risk of occasional misjudgment due to fatigue or visual oversight.<\/p>\n\n\n\nError Risks in High-Volume Assembly<\/strong><\/strong><\/h3>\n\n\n\nServo installation errors are particularly problematic because:<\/p>\n\n\n\n
\n- Orientation errors can affect mechanical movement<\/li>\n\n\n\n
- Incorrect models may cause compatibility issues<\/li>\n\n\n\n
- Model number mismatches can lead to traceability and even more serious problems<\/li>\n<\/ul>\n\n\n\n
Even small error rates can create rework costs and production delays<\/strong> when discovered later in the assembly process.<\/p>\n\n\n\n <\/strong><\/h2>\n\n\n\nThe Idea: Bringing Edge AI Vision to the Production Line<\/strong><\/h2>\n\n\n\nThe solution emerged through collaboration between Eric Pan, CEO of Seeed Studio, and the reCamera <\/a>team<\/strong><\/p>\n\n\n\nInstead of relying solely on human inspection, the team proposed deploying a compact AI camera<\/strong> directly at the inspection station.<\/p>\n\n\n\nThe goal was simple:<\/p>\n\n\n\n
\n- Automatically detect servo orientation<\/strong><\/li>\n\n\n\n
- Automatically verify servo model numbers<\/strong><\/li>\n\n\n\n
- Run the entire process locally at the edge<\/strong><\/li>\n<\/ul>\n\n\n\n
This approach would maintain production speed while improving inspection reliability.<\/p>\n\n\n\n
<\/strong><\/h2>\n\n\n\nThe Solution<\/strong><\/strong><\/h2>\n\n\n\nreCamera<\/a> is a fully open-source, modular AI camera. It runs Linux on an embedded AI SoC, delivers 1-3 TOPS computing power, and comes with built-in YOLO models and Node-RED. The PoE version<\/a>(which used in this case) is equipped with a 1\/2.9” CMOS sensor, replaceable M12 lenses (90\u00b0 FOV by default), PoE and GPIO. It is a network-ready smart eye for any system and also the go-to AI camera for YOLO at the edge.<\/p>\n\n\n\nThe final system uses 3 reCamera PoE<\/a> as an edge AI visual inspector<\/strong>, combining two complementary detection methods. This 3-reCamera multi-view inspection system<\/strong> is deployed to monitor the top, front, and right faces of the servo simultaneously, or can say the reCameras are positioned along the X, Y, and Z viewing axes<\/strong> to capture three orthogonal faces of the servo.<\/p>\n\n\n\n![\"\"\/](\"https:\/\/wdcdn.qpic.cn\/MTY4ODg1ODA1MzcyMTU3NA_432797_jBAnU-iVBRqK8x6A_1773123189?w=6048&h=4032&type=image\/jpeg\")
<\/figure>\n\n\n\n![\"\"\/](\"https:\/\/wdcdn.qpic.cn\/MTY4ODg1ODA1MzcyMTU3NA_190433_qLTtq_DUopsD0AKt_1773123240?w=6048&h=4032&type=image\/jpeg\")
<\/figure>\n\n\n\n1. Servo Orientation Detection (Optical Feature Extraction)<\/strong><\/strong><\/h3>\n\n\n\nTo determine whether a servo component is installed in reverse, the reCamera<\/a> team developed a custom optical binary algorithm<\/strong>.<\/p>\n\n\n\nThe process works as follows:<\/p>\n\n\n\n
\n- The reCamera<\/a> captures an image of the installed servo.<\/li>\n\n\n\n
- A binary image processing pipeline<\/strong> extracts key structural features.<\/li>\n\n\n\n
- Characteristic feature points on the servo housing are detected.<\/li>\n\n\n\n
- The system compares these features against a predefined orientation pattern.<\/li>\n<\/ol>\n\n\n\n
If the detected features do not match the expected configuration (2 lines), the system flags the servo as potentially reversed and highlights the corresponding detection area in red.<\/p>\n\n\n\n
This method is lightweight and highly efficient, allowing the detection to run directly on the edge device.<\/p>\n\n\n\n
2. Servo Specification Verification (OCR Recognition)<\/strong><\/strong><\/h3>\n\n\n\nIn addition to orientation detection, the system must verify that the servo model number and nameplate match the required specification<\/strong>.<\/p>\n\n\n\nThis is achieved by deploying an open-source OCR model directly on reCamera<\/a><\/strong>.<\/p>\n\n\n\nThe workflow:<\/p>\n\n\n\n
\n- The reCamera<\/a> captures the servo nameplate and model number<\/strong>.<\/li>\n\n\n\n
- The embedded OCR engine extracts the text information.<\/li>\n\n\n\n
- The extracted text is compared against the expected servo model database<\/strong>.<\/li>\n\n\n\n
- If the values match, the part passes inspection; otherwise, it is flagged in red.<\/li>\n<\/ol>\n\n\n\n
Because the OCR model runs entirely on-device<\/strong>, the system operates without cloud connectivity and delivers immediate results. Also guarantees data privacy for business consideration.<\/p>\n\n\n\n![\"\"\/](\"https:\/\/wdcdn.qpic.cn\/MTY4ODg1ODA1MzcyMTU3NA_561010_-TQUF4bw1Bb-AE4-_1773123973?w=1132&h=716&type=image\/jpeg\")
<\/figure>\n\n\n\n![\"\"\/](\"https:\/\/wdcdn.qpic.cn\/MTY4ODg1ODA1MzcyMTU3NA_380208_X-1ABLbTZMwPXKCv_1773123980?w=1132&h=716&type=image\/jpeg\")
<\/figure>\n\n\n\nThe two images above show the three views captured by the three reCameras<\/a> when the system starts, all focused on a single servo. The three detection panels below display clear, real-time inspection results. The time from placing a servo at the inspection position to obtaining the detection result is typically around 1\u20132 seconds<\/strong>.<\/p>\n\n\n\n <\/strong><\/h2>\n\n\n\nDeployment Architecture<\/strong><\/strong><\/h2>\n\n\n\nThe inspection system is designed for simple industrial deployment<\/strong>.<\/p>\n\n\n\nThe solution includes:<\/p>\n\n\n\n
\n- A dedicated mechanical fixture (jig)<\/strong> to position the component (servo) consistently<\/li>\n\n\n\n
- Pre-configured reCamera<\/a> hardware<\/strong><\/li>\n\n\n\n
- A packaged inspection program<\/strong> running locally on the device<\/li>\n<\/ul>\n\n\n\n
From the factory\u2019s perspective, deployment is straightforward:<\/p>\n\n\n\n
\n- Mount the fixture at the inspection station<\/li>\n\n\n\n
- Connect the reCamera<\/a> device<\/li>\n\n\n\n
- Power on the system<\/li>\n<\/ol>\n\n\n\n
The inspection pipeline runs automatically once the device is powered on. This design enables truly plug-and-play deployment without complex configuration<\/strong>.<\/p>\n\n\n\n <\/strong><\/h2>\n\n\n\nScalability for Production Lines<\/strong><\/strong><\/h2>\n\n\n\nIf the solution is rolled out across larger production lines, scaling is relatively simple.<\/p>\n\n\n\n
The system can be delivered as a pre-packaged inspection kit<\/strong>, including:<\/p>\n\n\n\n\n- reCamera<\/a> hardware<\/li>\n\n\n\n
- Pre-trained detection algorithms<\/li>\n\n\n\n
- OCR recognition software<\/li>\n\n\n\n
- Mechanical fixture<\/li>\n<\/ul>\n\n\n\n
This allows factory teams to install the system quickly without requiring deep AI expertise.<\/p>\n\n\n\n
<\/strong><\/h2>\n\n\n\nCurrent Status<\/strong><\/strong><\/h2>\n\n\n\nThe inspection system is currently in the final testing phase<\/strong> before full deployment to the factory production line.<\/p>\n\n\n\nOnce the system is fully integrated into the manufacturing workflow, it is expected to:<\/p>\n\n\n\n
\n- Reduce manual inspection workload<\/li>\n\n\n\n
- Improve consistency in servo installation checks<\/li>\n\n\n\n
- Prevent assembly errors earlier in the production process<\/li>\n<\/ul>\n\n\n\n
<\/strong><\/h2>\n\n\n\nWhy reCamera?<\/strong><\/strong><\/h2>\n\n\n\nUsing reCamera PoE<\/a> <\/strong>for industrial inspection provides several advantages:<\/p>\n\n\n\n\n- Edge AI processing with low latency<\/strong><\/li>\n\n\n\n
- Compact hardware suitable for factory deployment<\/strong><\/li>\n\n\n\n
- Flexible support for custom vision algorithms<\/strong><\/li>\n\n\n\n
- Stable thermal performance, 2\u20133W low power consumption<\/strong>, and PoE power supply<\/strong> enable reliable 24\/7 operation in factory environments<\/strong>.<\/li>\n\n\n\n
- All inspection data\u2014including component appearance and model number\u2014is processed locally on the device, protecting enterprise data privacy<\/strong>.<\/li>\n<\/ul>\n\n\n\n
For applications like component verification, label inspection, and assembly validation, edge<\/p>\n\n\n\n
Together with Pollen Robotics and Seeed Studio, <\/strong>Reachy Mini is the first open-source desktop robot introduced by Hugging Face<\/a><\/strong>, built to make experimenting with robotics and AI more accessible. Designed to be expressive, programmable, and easy to interact with, it allows users to explore human\u2013robot interaction and intelligent behavior right from their desk.<\/p>\n\n\n\n More than just a robot product, Reachy Mini functions as a development platform that encourages hands-on exploration and community collaboration. It provides an approachable way for people to prototype ideas, learn about robotics, and build interactive AI experiences.<\/p>\n\n\n\n As the production of Reachy Mini scaled up to over 3,000 pcs, one key step in the assembly process required operators to visually confirm:<\/p>\n\n\n\n (The circular component on the lower layer has two vertical lines on one side and a single vertical line on the other. In the correct assembly orientation, the side with two vertical lines should face outward, while the side with the single vertical line should face inward.)<\/p>\n\n\n\n This verification step occurs during the assembly inspection stage<\/strong>, where each unit must be checked before proceeding to final assembly.<\/p>\n\n\n\n Because this process relied entirely on human inspection, it introduced several operational risks:<\/p>\n\n\n\n To address this, the engineering team began exploring whether an edge AI vision system<\/strong> could automate this inspection step.<\/p>\n\n\n\n <\/p>\n\n\n\n Originally, this inspection step relied entirely on a human operator who visually checked every servo during the assembly process.<\/p>\n\n\n\n This process required one dedicated operator<\/strong> and still carried the risk of occasional misjudgment due to fatigue or visual oversight.<\/p>\n\n\n\n Servo installation errors are particularly problematic because:<\/p>\n\n\n\n Even small error rates can create rework costs and production delays<\/strong> when discovered later in the assembly process.<\/p>\n\n\n\n The solution emerged through collaboration between Eric Pan, CEO of Seeed Studio, and the reCamera <\/a>team<\/strong><\/p>\n\n\n\n Instead of relying solely on human inspection, the team proposed deploying a compact AI camera<\/strong> directly at the inspection station.<\/p>\n\n\n\n The goal was simple:<\/p>\n\n\n\n This approach would maintain production speed while improving inspection reliability.<\/p>\n\n\n\n reCamera<\/a> is a fully open-source, modular AI camera. It runs Linux on an embedded AI SoC, delivers 1-3 TOPS computing power, and comes with built-in YOLO models and Node-RED. The PoE version<\/a>(which used in this case) is equipped with a 1\/2.9” CMOS sensor, replaceable M12 lenses (90\u00b0 FOV by default), PoE and GPIO. It is a network-ready smart eye for any system and also the go-to AI camera for YOLO at the edge.<\/p>\n\n\n\n The final system uses 3 reCamera PoE<\/a> as an edge AI visual inspector<\/strong>, combining two complementary detection methods. This 3-reCamera multi-view inspection system<\/strong> is deployed to monitor the top, front, and right faces of the servo simultaneously, or can say the reCameras are positioned along the X, Y, and Z viewing axes<\/strong> to capture three orthogonal faces of the servo.<\/p>\n\n\n\n To determine whether a servo component is installed in reverse, the reCamera<\/a> team developed a custom optical binary algorithm<\/strong>.<\/p>\n\n\n\n The process works as follows:<\/p>\n\n\n\n If the detected features do not match the expected configuration (2 lines), the system flags the servo as potentially reversed and highlights the corresponding detection area in red.<\/p>\n\n\n\n This method is lightweight and highly efficient, allowing the detection to run directly on the edge device.<\/p>\n\n\n\n In addition to orientation detection, the system must verify that the servo model number and nameplate match the required specification<\/strong>.<\/p>\n\n\n\n This is achieved by deploying an open-source OCR model directly on reCamera<\/a><\/strong>.<\/p>\n\n\n\n The workflow:<\/p>\n\n\n\n Because the OCR model runs entirely on-device<\/strong>, the system operates without cloud connectivity and delivers immediate results. Also guarantees data privacy for business consideration.<\/p>\n\n\n\n The two images above show the three views captured by the three reCameras<\/a> when the system starts, all focused on a single servo. The three detection panels below display clear, real-time inspection results. The time from placing a servo at the inspection position to obtaining the detection result is typically around 1\u20132 seconds<\/strong>.<\/p>\n\n\n\n The inspection system is designed for simple industrial deployment<\/strong>.<\/p>\n\n\n\n The solution includes:<\/p>\n\n\n\n From the factory\u2019s perspective, deployment is straightforward:<\/p>\n\n\n\n The inspection pipeline runs automatically once the device is powered on. This design enables truly plug-and-play deployment without complex configuration<\/strong>.<\/p>\n\n\n\n If the solution is rolled out across larger production lines, scaling is relatively simple.<\/p>\n\n\n\n The system can be delivered as a pre-packaged inspection kit<\/strong>, including:<\/p>\n\n\n\n This allows factory teams to install the system quickly without requiring deep AI expertise.<\/p>\n\n\n\n The inspection system is currently in the final testing phase<\/strong> before full deployment to the factory production line.<\/p>\n\n\n\n Once the system is fully integrated into the manufacturing workflow, it is expected to:<\/p>\n\n\n\n Using reCamera PoE<\/a> <\/strong>for industrial inspection provides several advantages:<\/p>\n\n\n\n For applications like component verification, label inspection, and assembly validation, edge<\/p>\n\n\n\n\n
<\/p>\n\n\n\n
\n
\n
The Challenge<\/strong><\/strong><\/h2>\n\n\n\n
Manual Visual Inspection<\/strong><\/strong><\/h3>\n\n\n\n
Error Risks in High-Volume Assembly<\/strong><\/strong><\/h3>\n\n\n\n
\n
<\/strong><\/h2>\n\n\n\n
The Idea: Bringing Edge AI Vision to the Production Line<\/strong><\/h2>\n\n\n\n
\n
<\/strong><\/h2>\n\n\n\n
The Solution<\/strong><\/strong><\/h2>\n\n\n\n
<\/figure>\n\n\n\n
<\/figure>\n\n\n\n
1. Servo Orientation Detection (Optical Feature Extraction)<\/strong><\/strong><\/h3>\n\n\n\n
\n
2. Servo Specification Verification (OCR Recognition)<\/strong><\/strong><\/h3>\n\n\n\n
\n
<\/figure>\n\n\n\n
<\/figure>\n\n\n\n
<\/strong><\/h2>\n\n\n\n
Deployment Architecture<\/strong><\/strong><\/h2>\n\n\n\n
\n
\n
<\/strong><\/h2>\n\n\n\n
Scalability for Production Lines<\/strong><\/strong><\/h2>\n\n\n\n
\n
<\/strong><\/h2>\n\n\n\n
Current Status<\/strong><\/strong><\/h2>\n\n\n\n
\n
<\/strong><\/h2>\n\n\n\n
Why reCamera?<\/strong><\/strong><\/h2>\n\n\n\n
\n