Robofoot ÉPM Team Description - RoboCup2005 MiddleSize League
Bibliographic record
Abstract
Abstract. This paper presents some of the technical elements of the Robofoot team of soccer-playing robots developed for the Middle-Size Robot League of the RoboCup 2005. Since there are some solutions that are common to many teams, only the most recent and interesting developments that distinguishes our multi-robot system from others are presented. Specifically, the dual-kicker robot platform is robust and could lead to interesting playing capabilities for a diff-drive platform. The perception localization algorithms include data filtering and sharing mechanisms for different types of objects perceived in the environment. In order to detect various obstacles on the field in real time and without consuming too much processing power, a fast and versatile obstacle detection mechanism is used. This mechanism works in conjunction with a simple but effective method for obstacle avoidance based on a reactive approach. To implement their cooperative soccer-playing algorithms, each robot of the team can use its own Hierarchical Decision Machine, an architecture concept developed recently. Along with these distributed decision machines can exist a Decision Supervisor which has the capability to help coordination and control the play when needed. Much of this work is recent and still in development so some interesting things to come are noted to conclude the team description.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".