Towards a higher level of human-robot interaction and integration
Bibliographic record
Abstract
Spartacus, our 2005 AAAI Mobile Robot Challenge entry, integrated planning and scheduling, sound source localization, tracking and separation, message reading, speech recognition and generation, and autonomous navigation capabilities onboard a custom-made interactive robot. Integration of such a high number of capabilities revealed interesting new issues such as coordinating audio/visual/graphical capabilities, monitoring the impacts of the capabilities in usage by the robot, and inferring the robot’s intentions and goals. Our 2006 entry will be used to address these issues, add new capabilities to the robot and improving our software and computational architectures, with the objective of increasing the level, evaluating and improving our understanding of human-robot interaction and integration with an autonomous mobile platform. Challenges Designing a mobile robot that must operate in public settings probably addresses the most complete set of issues related to autonomous and interactive mobile robots, with system integration playing a fundamental role at all levels. Reported issues are 1) the robust integration of different software packages and intelligent decision-making capabilities; 2) natural interaction modalities in open settings; 3) adaptation to environmental changes for localization; and 4) monitoring/reporting decisions made by the robot (Gockley
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".