Upload README.md

README.md

@@ -23,6 +23,10 @@ At a 20 Hz control loop that is 0.015 % of the cycle budget: small is the point,
 
 (An earlier revision of this card said 116 parameters. That count included the file's metadata arrays. The learnable weight and bias count is 101.)
 
+Why does a company focused on predictive maintenance publish a robot driving model?
+
+Because this robot is our test bed: the same recipe of tiny numpy networks, measured numbers, and self QA/QC on every stage is what we now focus on the predictive maintenance (PdM) of aging mechanical equipment.
+
 This is the smaller, older sibling of
 [NCDTech/sim-driving-mlp-numpy](https://huggingface.co/NCDTech/sim-driving-mlp-numpy).
 
@@ -116,6 +120,8 @@ The hardware itself, with the ultrasonic sensors on the hand:
 
 Together they show the path we actually took: drive the real robot first, feel the data problems, then build the simulation studio with self QA/QC wired into every stage.
 
+That same discipline is now focused on predictive maintenance for aging mechanical equipment.
+
 ## 한국어
 
 **영상 속에서 실제로 로드되어 돌던 바로 그 가중치 파일**입니다 (영상의 파일 열기 장면에서 파일명을 추적해 확인).
@@ -142,4 +148,6 @@ Together they show the path we actually took: drive the real robot first, feel t
 
 바로 이런 구멍을 잡으려고 지금의 저희 시스템은 모든 단계에 셀프 QA/QC(클래스 분포 검사 포함)를 심었습니다.
 
+이 로봇은 저희의 테스트베드이고, 같은 기술을 지금은 노후 기계식 설비의 예지보전(PdM)에 집중하고 있습니다.
+
 Learn more: https://huggingface.co/NCDTech · https://ncdtech.org · 주행 영상: https://youtu.be/6DJX0T6qrtM