A Versatile Design Platform for a Walking Robot in Harsh Environments
Bibliographic record
Abstract
Abstract In a previous paper [1] on the design of Capra — a quadruped robot with improved agility, we had presented the main design avenues that were to be considered for the development of an agile mobile walking robot, Capra. The objective of achieving full and autonomous robot agility in harsh environments has always been predominant in our research program on robot design for improved capabilities and applications potential. In this follow-up paper, we report the design results to meet another level of objectives that we had previously determined and currently are incorporating in the Capra hierarchy. Systematic design methodologies that were developed for the robotic machine structure also enable one to consider the Capra robot as not only an agile walking-machine but also as a versatile design platform. It can be deployed for retrofit in many automation applications or for specific uses in harsh environments involving critical design constraints. This design approach has resulted in the development and integration of many of Capra’s fundamental sub-systems by means of a range of modules and prototypes. This paper addresses all the different design issues involved in the development of a versatile design platform for a truly mobile robot. The design methodologies to support the various capabilities are presented here as independent systems, which can be fitted, integrated and retrofitted for any mobile robot configuration. Several design results are presented as examples of the design process.
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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 | 0.000 |
| 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 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".