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README.md

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 ---
 license: cc-by-4.0
+tags:
+  - robotics
+  - mlp
+  - numpy
+  - obstacle-avoidance
+  - simulation
+pretty_name: Sim Driving MLP (NumPy)
 ---
+
+# Sim Driving MLP — 279-parameter obstacle-avoidance policy (NumPy)
+
+A deliberately tiny neural network: **4 ultrasonic distances in, one driving command
+out.** 279 parameters, float32, trained and verified entirely inside our robot
+simulation studio. Small enough to read, small enough to run on an MCU-class device.
+
+**Trained on simulation data from a virtual map — no real-world or customer data.**
+
+## Architecture
+
+```
+input 4  →  dense 16 (ReLU)  →  dense 8 (ReLU)  →  output 6
+```
+
+| part | meaning |
+|------|---------|
+| input (4) | ultrasonic distances: **F**ront, **L**eft, **R**ight, **B**ack (normalized with the included `norm_mean` / `norm_std`) |
+| output (5 + 1) | 5 command logits — `FWD / LEFT / RIGHT / STOP / BACK` — plus 1 turning-angle regression head |
+
+## Metrics (validation, virtual map)
+
+| metric | value |
+|--------|-------|
+| command accuracy | **97.2 %** (best epoch 529 / 572) |
+| turning-angle MAE | ~6.9° |
+
+The `.npz` also embeds `meta_json`: the full 572-epoch training history
+(per-class accuracy, loss, angle MAE per epoch), so the training curve is inspectable.
+
+## Load and run (NumPy only, no framework)
+
+```python
+import numpy as np
+
+d = np.load("sim_driving_mlp.npz")
+x = np.array([[120.0, 45.0, 200.0, 300.0]])  # F, L, R, B distances (mm)
+x = (x - d["norm_mean"]) / d["norm_std"]
+
+h = np.maximum(x @ d["W0"] + d["b0"], 0)
+h = np.maximum(h @ d["W1"] + d["b1"], 0)
+y = h @ d["W2"] + d["b2"]
+
+cmd = ["FWD", "LEFT", "RIGHT", "STOP", "BACK"][int(np.argmax(y[0, :5]))]
+angle = float(y[0, 5])
+print(cmd, angle)
+```
+
+## Where this fits: our 4-layer stack
+
+This model is a **layer-2 artifact** of our stack — an edge neural network verified
+through the 8-stage physics simulation workflow of our robot simulation studio.
+Every stage of that workflow runs self QA/QC (layer 3) and reports through an
+on-premise conversational LLM (layer 4); the record formats those layers produce
+are shown in our companion dataset:
+[NCDTech/human-gated-qaqc-knowledge-example](https://huggingface.co/datasets/NCDTech/human-gated-qaqc-knowledge-example)
+
+## 한국어
+
+**초음파 4방향 거리(전·좌·우·후)를 넣으면 주행 명령이 나오는 279 파라미터짜리 작은
+신경망**입니다. 저희 로봇 시뮬레이션 스튜디오의 8단계 워크플로(데이터 수집 → 학습 →
+평가 → 물리 시뮬레이션 검증)를 통과한 계층 2(엣지 신경망) 산출물이며, **가상 맵
+시뮬레이션 데이터로만 학습**했습니다 — 실데이터·고객 데이터 없음.
+
+출력은 주행 명령 5클래스(전진/좌회전/우회전/정지/후진) + 회전각 회귀 1개.
+검증 정확도 97.2 %, 각도 오차 약 6.9°. 파일 안에 정규화 통계와 572 에포크 학습
+이력 전체가 함께 들어 있어 학습 곡선을 그대로 확인할 수 있습니다.
+
+Learn more: https://huggingface.co/NCDTech · https://www.ncdtech.org

sim_driving_mlp.npz

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