Having a robot attend AAAI 2000
Bibliographic record
Abstract
Having robot assistants represent us in meetings, shop for us, or do chores, for instance, would be useful. But to do so, robots must be able to face the contingencies of the real world by making the most of their sensing, actuating, processing, and reasoning abilities. To promote research efforts in that direction, the AAAI has been organizing the Mobile Robot Challenge since 1999. This initiative aims to present the robotics community with a new challenge that drives ongoing research and provides an effective public venue for demonstrating significant new work. The task is to make a robot attend the National Conference on AI. The robot is placed at the conference center's front door and must navigate to the registration desk by following signs and asking for directions. At the registration desk, the robot receives a map of the conference hall, a destination conference room, and a deadline by which to reach it. While going to the conference room, the robot might have to take the elevator, schmooze with important people, or handle additional tasks such as guarding a room for a few minutes. When the robot reaches the conference room, it must give a two-minute presentation about itself. This past August the authors entered their robot, Lolitta Hall, into the competition at AAAI 2000 in Austin, Texas. The robot is a a Pioneer 2 robot with 16 sonars, a pan-tilt-zoom camera, a Pentium MMX 233-MHz onboard computer, and an infrared ring for detecting the charging station. Lolitta's integrated skills are described and discussed.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.100 |
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".