Autonomous Stair Climbing with Reconfigurable Tracked Mobile Robot
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
Mobile robots have been developed for surveillance, reconnaissance and inspection as well as for operation in hazardous environments. Some are intended to explore not only natural terrains but also artificial environments, including stairs. This paper explores algorithms to autonomously climb stairs. The algorithms were derived and implemented for a specific mobile robot with the ability to traverse such obstacles by changing its tracks configuration. Furthermore, algorithms have been developed for conditions under which the mobile robot halts its motion during the climbing process when at risk of flipping over or falling down. The technical problems related to the implementation of some of these functions have been identified and analyzed, and their solutions validated and tested. The algorithms and solutions were validated experimentally, illustrating the effectiveness of autonomous climbing of stairs.
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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 | 0.000 | 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) | 0.001 | 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