A brochette of socially interactive robots
Bibliographic record
Abstract
The design of interactive mobile robots is a multidisciplinary endeavor that profits from putting robots with people and studying their effects and impacts. To do so, two main issues must be addressed: giving robots capabilities in order to interact in meaningful and efficient ways with people, and the ability to move in human settings. This paper briefly describes four robotic platforms that are going to be demonstrated at the AAAI 2005 Robot Competition. Interactive Capabilities for Mobile Robots Spartacus is the robotic platform we have designed for highlevel interaction with people in real life settings. The robot built is shown left in Figure 1 and is equipped with a SICK LMS200 laser range finder, Sony SNC-RZ30N 25X pan-tiltzoom color camera, an array of eight microphones placed in the robot’s body, a touch screen interface, an audio system, one on-board computer and one laptop computer. The robot is also equipped with a business card dispenser, which is part of the robot’s schmoozing strategy. Numerous algorithms must be integrated to provide Spartacus with interactive capabilities. MARIE (Mobile and Autonomous Robot Integrated Environment) is our middleware programming environment allowing multiple applications, operating on one or multiple machines/OS, to work together in order to facilitate program reusability. MARIE currently links Player/Stage/Gazebo (Vaughan, Gerkey, & Howard 2003), CARMEN (Montemerlo, Roy, & Thrun
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 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".