Active balancing and turning for alpine skiing robots
Bibliographic record
Abstract
Abstract This paper presents our preliminary research into the autonomous control of an alpine skiing robot. Based on our previous experience with active balancing on difficult terrain and developing an ice-skating robot, we have implemented a simple control system that allows the humanoid robot Jennifer to steer around a simple alpine skiing course, brake, and actively control the pitch and roll of the skis in order to maintain stability on hills with variable inclination. The robot steers and brakes by using the edges of the skis to dig into the snow, by inclining both skis to one side the robot can turn in an arc. By rolling the skis outward and pointing the toes together the robot creates a snowplough shape that rapidly reduces its forward velocity. To keep the skis in constant contact with the hill we use two independent proportional-integral-derivative (PID) controllers to continually adjust the robot’s inclination in the frontal and sagittal planes. Our experiments show that these techniques are sufficient to allow a small humanoid robot to alpine ski autonomously down hills of different inclination with variable snow conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".