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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".