README.md

metadata

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

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.

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

한국어

초음파 4방향 거리(전·좌·우·후)를 넣으면 주행 명령이 나오는 279 파라미터짜리 작은 신경망입니다. 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.

Learn more: https://huggingface.co/NCDTech · https://www.ncdtech.org