On Bisection Continuous Collision Checking Method: Spherical Joints and Minimum Distance to Obstacles
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Bibliographic record
Abstract
In this paper, we adapt the Continuous Collision Detection (CCD) method proposed in [1] to efficiently handle the case of spherical and two revolute joints, this kind of joints is very common in modern robotic systems. The new formulations provide more tight motion bounds, thus increase the success rate of checking collision-free paths. We also propose an extension to get the minimum distance to obstacles along a path, this information is primordial as it allows sampling-based motion planning techniques to sort collision-free paths according to their minimum clearance. We have integrated our implementation into a sampling-based motion planning technique and validated it through simulation and on the real Baxter research robot. The experiments revealed that the method not only does not miss any collision between the robot and the obstacles, but also the minimum distance extension provides the path with the maximum clearance at no additional computational cost.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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