Towards a DIY Robotic Tail Toolkit for Future Service Robot and Computing Education
Bibliographic record
Abstract
A service robot is an Internet of Things (IoT) or a cyberphysical system comprising a robotic body integrated with one or more Cloud-based services. This architecture enables sophisticated human-machine interaction and expands the functional capabilities of conventional robotic systems. A service robot can take the form of anthropomorphic, zoomorphic, or even theomorphic design. Our research team is collaborating with the Canadian National Institute for the Blind (CNIB) to develop a prototype service dog robot. Due to this, this paper presents our current experiences in designing, developing, and evaluating a Do-It-Yourself (DIY) articulated robotic tail toolkit for zoomorphic robots at Ontario Tech University. We detail the design process that led to the development of a robotic tail mechanism, driven by microservices, which emphasizes reliability and expressive while utilizing readily accessible components. We range also conducted experiments and evaluations of the robotic tail during a hands-on workshop with students from diverse backgrounds, using a services computing approach. We continue to develop an effective DIY educational tool that promotes student engagement in Human-Robot Interaction (HRI) design for future service computing education.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".