Overcoming unknown occlusions in eye-in-hand visual search
Bibliographic record
Abstract
We propose a method for handling persistent visual occlusions that disrupt visual tracking for eye-in-hand systems. Our approach allows a robot to “look behind” an occluder and re-acquire its target. To allow efficient planning, we avoid exhaustive mapping of the 3D occluder into configuration space, and instead use informed samples to strike a balance between target search and information gain. A particle filter continuously estimates the target location when it is not visible. Meanwhile, we build a simple but effective map of the occluder's extents to compute potential occlusion-clearing motions using very few calls to efficient approximations of inverse kinematics. Our mixed-initiative cost function balances the goal of directly locating the target with the goal of gaining information through mapping the occluder. Monte-Carlo optimization with efficient data-driven proposals allows us to approximate one-step solutions efficiently. Experimental evaluation performed on a realistic simulator shows that our method can quickly obtain clear views of the target, even when occlusions are persistent and significant camera motion is required.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".