Selective Attention by Perceptual Filtering in a Robot Control Architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern autonomous robots must integrate multiple perceptual and behavioral modalities to be useful in our daily lives. Such integration is constrained by the limited onboard computing capacity of robotic platforms. To alleviate this issue, perceptual filtering, a selective attention mechanism, can be used to efficiently manage computing resources based on what the robot has to accomplish. This paper describes our implementation of perceptual filtering in a robot control architecture, implemented using robot operating system (ROS), and how it can dynamically optimize the use of the computing resources available on the robot. Our perceptual filtering mechanism is demonstrated and validated using a mobile humanoid platform integrating autonomous and teleoperated navigation, QR code recognition, face recognition, and sound localization capabilities.
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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) | 0.000 | 0.000 |
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 it