Overcoming occlusions in eye-in-hand visual search
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we propose a method for handling persistent visual occlusions that disrupt visual tracking for eye-in-hand systems. This approach provides an efficient strategy for the robot to “look behind” the occlusion while respecting the robot's physical constraints. Specifically, we propose a decoupled search strategy combining a naïve pan tilt search with a sensor placement approach, to reduce the strategy's computational cost. We proceed by mapping limited environmental data into the robot configuration space and then planning within a constrained region. We use a particle filter to continuously estimate the target location, while our configuration-based cost function plans a goal location for the camera frame, taking into account robot singularity, self-collision and joint limit constraints. To validate our algorithm, we implemented it on an eye-in-hand robot system. Experimental results for various situations support the feasibility of our approach for quickly recovering fully occluded moving targets. Finally we discuss the implications of this approach to mobile robot platforms.
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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