Trajectory specification via sparse waypoints for eye-in-hand robots requiring continuous target visibility
Bibliographic record
Abstract
This paper presents several methods of managing field of view constraints of an eye-in-hand system for vision- based pose control with limited controller input. Herein, the possible inverse kinematic solutions for a desired relative camera pose are evaluated to determine whether the interpolated trajectories satisfy field of view constraints for the target of interest. If no immediately feasible trajectory exists, additional waypoints are specified to guide the robot towards its goal while maintaining visibility. The insertion of an additional visible and feasible waypoint divides the problem into two sub-problems of the same form, but of lesser difficulty by reducing the robot's interpolation distance. Virtual image-based visual servoing (IBVS) is used to generate an ideal image trajectory to guide the selection of waypoints. A damped least- squares inverse kinematics solution is implemented to handle robot singularities. The methods are simulated for a CRS-A465 robot with a Sony XC-HR70 camera.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".