license: cc-by-4.0
tags:
- robotics
- mlp
- numpy
- obstacle-avoidance
- simulation
- edge
- embedded
- industrial
- sensor-data
pretty_name: Sim Driving MLP (NumPy)
Sim Driving MLP: a 270-parameter obstacle-avoidance policy (NumPy)
270 learnable parameters · 1.05 KB of weights · 0.0103 ms per inference · 1.95 KB peak inference memory · numpy only, no framework.
Inference numbers measured on a 12th-gen i7 laptop CPU, average over 100,000 runs.
(An earlier revision of this card said 279 parameters. That count included the file's normalization and metadata arrays. The learnable weight and bias count is 270.)
A deliberately tiny neural network: 4 ultrasonic distances in, one driving command out.
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
| part | meaning |
|---|---|
| input (4) | ultrasonic distances: Front, Left, Right, Back (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.
The plot below is drawn directly from that embedded history:
Load and run (NumPy only, no framework)
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)
The simulator and the target robot
The studio stage where the model's four inputs are defined: ultrasonic sensors F/L/R/B with datasheet-based noise models, checked by the built-in self QA/QC checklist.
On the right, the local LLM explains a warning from that checklist, citing the stage report as its basis.
This screen is a frame from our demo video (22 min, Korean).
A driving run on the 8 m × 8 m virtual map used for data collection (green: ultrasonic rays from the robot):
And this exact model running in the studio's 3D evaluation stage.
The left panel shows the live inference at the current step: the four sensor inputs (F/L/R/B, mm), the softmax over the five commands with FORWARD selected, and the angle head:
The target hardware: a tracked test robot with the ultrasonic sensors mounted on the hand, the same F-channel placement the simulator reproduces.
Where this fits: our 4-layer stack
(Diagram is in Korean; it is the same figure used on our website, demo video, and companion dataset.)
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
We now focus this stack on predictive maintenance (PdM) for aging mechanical equipment.
한국어
초음파 4방향 거리(전·좌·우·후)를 넣으면 주행 명령이 나오는 270 파라미터짜리 작은 신경망입니다.
추론 1회 0.0103 ms, 추론 메모리 1.95 KB, 의존성은 numpy 하나 (12세대 i7 노트북 CPU에서 10만 회 평균 실측).
(이전 판의 "279 파라미터"는 정규화·메타 배열까지 센 수치였고, 학습 가중치 기준 정확한 수는 270입니다.)
저희 로봇 시뮬레이션 스튜디오의 8단계 워크플로(데이터 수집 → 학습 → 평가 → 물리 시뮬레이션 검증)를 통과한 계층 2(엣지 신경망) 산출물이며, 가상 맵 시뮬레이션 데이터로만 학습했습니다 (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.
저희는 이 스택을 노후 기계식 설비의 예지보전(PdM)에 집중하고 있습니다.
Learn more: https://huggingface.co/NCDTech · https://ncdtech.org · Demo video: https://youtu.be/ftsw_vbfw6E