An anticipative kinematic limitation avoidance algorithm for collaborative robots: Two-dimensional case
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Bibliographic record
Abstract
This paper presents an anticipative robot kinematic limitation avoidance algorithm for collaborative robots. The main objective is to improve the performance and the intuitivity of the physical human-robot interaction. One obstacle to achieve this goal is the management of limitations such as joint position limitation, singularities and collisions with the environment. Indeed, in addition to performing a given principal task, human users must pay a close attention to the manipulator configuration in order to handle the kinematic limitations. The proposed anticipative algorithm aims at relieving the human user from having to deal with such limitations. The algorithm is first presented and detailed for each individual limitation of a planar RR serial robot. The framework developed to manage several limitations occurring simultaneously is then presented. Finally, experiments are performed in order to assess the performance of the algorithm.
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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) | 0.000 | 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 | 0.000 |
| 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