Inter-humanoid robot interaction with emphasis on detection: a comparison study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Robot Interaction has always been a challenge in collaborative robotics. In tasks comprising Inter-Robot Interaction, robot detection is very often needed. We explore humanoid robots detection because, humanoid robots can be useful in many scenarios, and everything from helping elderly people live in their own homes to responding to disasters. Cameras are chosen because they are reach and cheap sensors, and there are lots of mature two-dimensional (2D) and 3D computer vision libraries which facilitate Image analysis. To tackle humanoid robot detection effectively, we collected a data set of various humanoid robots with different sizes in different environments. Afterward, we tested the well-known cascade classifier in combination with several image descriptors like Histograms of Oriented Gradients (HOG), Local Binary Patterns (LBP), etc. on this data set. Among the feature sets, Haar-like has the highest accuracy, LBP the highest recall, and HOG the highest precision. Considering Inter-Robot Interaction, it is evident that false positives are less troublesome than false negatives, thus LBP is more useful than the others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it