A Multi-Rate Control Scheme for a Robotic Eye/Head System Integrating Visual and Self-Motion Cues
Bibliographic record
Abstract
In primates, the vestibulo-ocular reflex (VOR) is known to stabilize gaze during head perturbations. Also, the internal brain circuits controlling eye movements are found to operate with neural delays much smaller than delays in visual processing pathways (~2ms vs 150 ms). Based on these biological findings, we present a unified multi-rate biomimetic gaze controller integrating VOR mechanisms (self-motion cues) with tracking (pursuit and saccades) for a robotic head with two cameras. The controller uses automatic parametric switching in shared premotor circuits to alternate between two movement types: smooth pursuit (slow phase) relying on visual feedback, and fast blind corrective jumps (fast phase) producing nystagmus. During fixation or tracking of a target (slow phase), a head-motion sensor (VOR) detects head rotation direction and drives the cameras in the opposite direction so that gaze in space remains on the visual target. A multi- rate control scheme is used to overcome inherent delays in the visual system limited to a 30Hz frame rate. Adding prediction and memory (PDI controller) in the visual feedback copes better with visual delays and allows slow tracking bandwidths near 2Hz. The rest of the controller operates at 600Hz: since the saccade circuit is effectively blind, the higher rate controller operation allows increasing saccade bandwidths without ringing to over 30Hz. In this paper, we describe the controller model and we present simulation results to demonstrate its performance.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".