Miniature-Scale Radio-Controlled Excavator Robot: Expedient to Automated3D and 2D Poses Labeling
Bibliographic record
Abstract
생산성, 안정성, 수익률 향상, 그리고 젊은 노동인구의 부족 현상에 대응하기 위해선 robotic automation and digitization으로 대변되는 Industry 4.0으로의 기술혁신이 필요하다. 본 연구에서는 건설로봇 연구개발(R&D)을 지원하기 위한 하나의 수단으로써, miniature-scale radio-controlled(RC) equipment의 사용을 제안하고자 한다. 시각적 인공지능(Visual AI)은 Industry 4.0의 핵심 기술이지만, 방대한 양의 학습데이터를 요구한다. 하지만, 실 현장에서 작업중인 중장비의 데이터를 수집 및 레이블링 하는 것은 현실적으로 불가능 하다. 본 연구는 시중의 미니어처 엑스커베이터의 RC 시스템을 커스터마이즈하여, 기존의 컨트롤러를 사용하지 않고, PC를 통해 보다 구체적인 명령을 내릴 수 있도록 변경하였다. 버켓모션에 대한 사전검증 결과, 150 번의 시도에서 약 1-2° 내외의 mean average deviation(MAD)(|예측값 - 측정값|)를 확인 하였다. 본 연구에서는 이 경제적인 시스템을 활용한 중장비의 3D/2D 포즈 레이블링 방법을 제안하고자 한다.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".