sazzysara21/real-robot-driving-sessions
Real-Robot Driving Sessions: the 18 minutes that trained our 101-parameter driver Three real driving sessions of our tracked test robot, recorded at 20 Hz on the test floor. These are the exact sessions that trained NCDTech/real-robot-driving-mlp-numpy, the weight file shown running in this video. We can prove the lineage. Our desktop studio logs every training run, and the entry for that weight file reads: 2026-04-24 16:14:25 | build_dataset 시작: 3 파일 —… See the full description on the dataset page: https://huggingface.co/datasets/sazzysara21/real-robot-driving-sessions.
Real-Robot Driving Sessions: the 18 minutes that trained our 101-parameter driver
Three real driving sessions of our tracked test robot, recorded at 20 Hz on the test floor.
These are the exact sessions that trained [NCDTech/real-robot-driving-mlp-numpy](https://huggingface.co/NCDTech/real-robot-driving-mlp-numpy), the weight file shown running in this video.
We can prove the lineage.
Our desktop studio logs every training run, and the entry for that weight file reads:
2026-04-24 16:14:25 | build_dataset 시작: 3 파일 —
session_20260417_165126.bin, session_20260417_154754.bin, session_20260421_142743.binThose three files are this dataset.
What one session looks like
Files
Each session ships in two forms.
.csv is the accessible form: 71 columns, one row per 50 ms sample.
.bin is the untouched original written by the robot's recorder (parsed with zero dropped chunks and zero truncated bytes).
Columns (CSV)
The model used only six features from all of this: front/left/right distances plus their frame-to-frame deltas.
Everything else is here because the robot logged it anyway, and multimodal robot telemetry is rare at this size.
Motor currents, 36 IMU channels, and environment sensors at a fixed 20 Hz: this is the same recording discipline we now focus on the predictive maintenance (PdM) of aging mechanical equipment.
Load it
import pandas as pd
df = pd.read_csv("session_20260417_165126.csv")
auto = df[df.ctrl_source == "AUTO"]
print(len(df), "samples,", len(auto), "in AUTO mode")Honest notes
Total driving time is about 18 minutes.
That is genuinely small, and it shows in the model: STOP and BACK actions barely occur in these sessions, so the trained network scored 0 % on those classes.
We publish the data anyway, because this is what real early-stage robot data looks like, and because exactly this gap is why our current systems run a class-distribution check inside self QA/QC on every stage.
You can watch that self QA/QC system working, and chatting about its own reports through an on-premise LLM, in our 22-minute demo video.
pos_x_mm / pos_y_mm are dead-reckoning estimates and drift over time.
IMU channels are raw int16 readings, unscaled.
ultra_front_mm comes from the hand-mounted sensor: on this robot the front ultrasonic rides on the hand (per our 2026-03-31 sensor mapping sheet), and ultra_grip_mm is a spare channel.
한국어
궤도 시험 로봇의 실주행 세션 3개(20 Hz, 총 16,928샘플, 약 18분)입니다.
real-robot-driving-mlp-numpy 모델을 학습시킨 바로 그 파일들이며, 계보는 학습 당시 앱 로그(위 인용)로 증명됩니다.
각 세션은 원본 .bin(기록기 출력 그대로)과 변환 .csv(71컬럼: 초음파 5방+IMU 36채널+모터 PWM/전류+자세+카메라+오디오+환경) 두 형태로 제공됩니다.
모터 전류·IMU 36채널·환경 센서를 고정 20 Hz로 기록하는 이 계측 규율을, 지금은 노후 기계식 설비의 예지보전(PdM)에 집중해 쓰고 있습니다.
정직 고지: 총 18분짜리 작은 데이터라 정지/후진 동작이 거의 없고, 그래서 학습된 모델의 해당 클래스 정확도가 0%였습니다.
이런 구멍을 잡으려고 지금의 저희 시스템은 모든 단계에 클래스 분포 검사를 포함한 셀프 QA/QC를 심었습니다.
그 셀프 QA/QC가 실제로 돌아가며 온프레미스 LLM과 대화하는 모습은 22분 데모 영상에서 볼 수 있습니다.
Learn more: https://huggingface.co/NCDTech · https://ncdtech.org · 주행 영상: https://youtu.be/6DJX0T6qrtM · Self QA/QC demo: https://youtu.be/ftsw_vbfw6E · More robot footage: https://huggingface.co/spaces/NCDTech/robot-lab