Evaluation of graspable region and selection of footholds for biped pole-climbing robots
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For biped pole-climbing robots (BiPCRs), footholds, indicating a sequence of discrete gripping points from the start to the goal, are necessary for travelling in trussed environments. Graspable region in each climbing cycle is the valuable priori knowledge for consideration of selecting specific grasping point. However, how to evaluate all the grasps within the graspable region so as to provide a reasonable grasp selection strategy, is an interesting and pending issue. In this paper, we present three criteria to evaluate grasps considering the characteristics of the climbing motion of BiPCRs. Based on these criteria, we also propose two strategies to optimally select a grasp from the graspable region. An algorithm is presented for BiPCRs to perform foothold planning in trusses. Simulations are carried out to verify the effectiveness of the criteria, the selection strategies and the foothold planning 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 | 0.000 |
| 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