Fire Fighting Robot Competitions And Learning Outcomes – A Quantitative Assessment
Bibliographic record
Abstract
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 2332 Fire-Fighting Robot Competitions and Learning Outcomes: A Quantitative Assessment Igor M. Verner, David J. Ahlgren, Jacob E. Mendelssohn Technion -- Israel Institute of Technology/ Trinity College, Hartford Abstract This paper presents a quantitative assessment of the Trinity College Fire-Fighting Home Robot Contest, the largest robot contest open to designers of any age, affiliation and experience [1,2]. Our assessment develops a profile of the participants, and it evaluates factors that motivate the participants, including interest in designing robots, interest in science and technology, career opportunities, and engagement of robotics as a hobby. The paper also evaluates participants’ progress in eight key disciplines related to robot design, including electronics, teamwork, system design, and programming. I. Introduction The Trinity College Fire-Fighting Home Robot Contest is the largest robotics competition in the world that is open to contestants of any age, affiliation, ability, and experience. The contest offers a design challenge that can be addressed at varying levels of technical sophistication, and it has attracted NASA scientists and professional engineers, college and high school students, and even fourth-graders. Fully operational robots have been built by single individuals and by teams as large as 15 persons. The event has been covered widely in the print media with articles in Electronic Design, Popular Mechanics, Circuit Cellar INK, Byte, the London Times, and the New York Times. Regional fire-fighting contests have been held in Calgary, Seattle, Fort Worth, and Philadelphia. In 2000 a regional contest will involve both secondary-school and university- level design teams in Israel. Of the 87 robots entered in the 1999 Trinity fire-fighting contest, 73 robots, involving 237 team members, actually competed. In the last two contests some 35 college and university teams have competed in the contest’s senior division, and 28 accepted the design challenge in 1999. Entrants included persons from Tufts, Yale, M.I.T., the U.S. Air Force Academy, Penn State University, Trinity, University of Texas, New Mexico Tech., Ohio State, Drexel, the U.S. Naval Academy, the Swiss Federal Institute of Technology, and Chiang Mai University in Thailand. Moreover, thirty-two robots were entered in the 1999 contest’s junior (K-12) division. High-school participants came from Michigan, New Jersey, New York, Ontario and Pennsylvania, middle- school teams came from Georgia and New York. There were also five high-school teams with 24
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".