Autonomous Robot that Uses Symbol Recognition and Artificial Emotion to Attend the AAAI Conference
Bibliographic record
Abstract
This paper describes our approach in designing an autonomous robot for the AAAI Mobile Robot Challenge, making the robot attend the National Conference on AI. The goal was to do a simplified version of the whole task, by integrating methodologies developed in various research projects conducted in our laboratory. Original contributions are the use of a symbol recognition technique to make the robot read signs, artificial emotion for expressing the state of the robot in the accomplishment of its goals, a touch screen for human-robot interaction, and a charging station for allowing the robot to recharge when necessary. All of these aspects are influenced by the different steps to be followed by the robot attendee to complete the task from start-to-end. Introduction LABORIUS is a young research laboratory interested in designing autonomous systems that can assist human in real life tasks. To do so, robots need some sort of "social intelligence ", giving them the ability to ...
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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; both teacher heads agree on what is shown here.
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".