"Excuse me, where's the registration desk?" Report on Integrating Systems for the Robot Challenge AAAI 2002
Bibliographic record
Abstract
In July and August 2002, five research groups -- Carnegie Mellon University, Northwestern University, Swarthmore College, Metrica, Inc., and the Naval Research Laboratory -- collaborated and integrated their various robotic systems and interfaces to attempt The Robot Challenge held at the AAAI 2002 annual conference in Edmonton, Alberta. The goal of this year's Robot Challenge was to have a robot dropped off at the conference site entrance; negotiate its way at the site, using queries and interactions with humans and visual cues from signs, to the conference registration area; register for the conference; and then give a talk. Issues regarding human/robot interaction and interfaces, navigation, mobility, vision, to name but a few relevant technologies to achieve such a task, were put to the test. In this report we, the team from the Naval Research Laboratory, will focus on our portion of The Robot Challenge. We will discuss some lessons learned from collaborating and integrating our system with our research collaborators, as well as discuss what actually transpired -- what worked and what failed -- during the robot's interactions with conference attendees in achieving goals. We will also discuss some of the informal findings and observations collected at the conference during the interaction and navigation of the robot to complete its various goals.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.077 | 0.052 |
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".