Self-motion graph in path planning for redundant robots along specified end-effector paths
Why this work is in the frame
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Bibliographic record
Abstract
We consider the problem of planning collision-free paths for a redundant robot manipulator whose end-effector must travel along a specified path. A probabilistic method has been proposed for the problem, which does not allow self-motions of the robot as it moves along the end-effector path. In this paper, we propose an enhancement, which allows such self-motions. This is primarily accomplished by explicitly representing self-motions for a certain pose as a self-motion graph, which is explored with probabilistic techniques for closed-chain robots. Computer simulations show that this enhancement improves performance in most cases. Depending on the limits set on the run-time (always needed in practice for probabilistic sampling methods), the planner with self-motion enhancement will find a path where the original algorithm without self-motion may not
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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 | 0.000 |
| Open science | 0.001 | 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