Bibliographic record
Abstract
Industrial robots for working in factory environment were widely researched and lead enormous development in the 20th century.But the research subjects are moving to Service Robotics with the busy life style of humans in the 21st century.Humans have great concern in healthy life and do not want to get 3D jobs (difficult, dangerous and dirty) as well as repeated simple jobs.For these reasons, Service Robots which do these jobs instead of humans are the main focus of research nowadays.As Service Robots perform their jobs in the same environment as humans, Service Robots should have essential abilities humans have.They should recognize faces, gestures, characters, objects, speech and atmosphere.They should find their way to reach the goal without collisions and destructions, and accomplish the task at hand successfully.They should grab and deliver some objects.They should communicate with humans based on emotion.These all research subjects are included in Service Robotics area.This book consists of 18 chapters about current research results of service robots.Topics covered include various kinds of service robots, development environments, architectures of service robots, Human-Robot Interaction, networks of service robots and basic researches such as SLAM, sensor network, etc.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.022 |
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".