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Record W2521052416 · doi:10.1017/s0269888916000163

Active balancing and turning for alpine skiing robots

2016· article· en· W2521052416 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Knowledge Engineering Review · 2016
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsUniversity of Manitoba
FundersUniversity of Manitoba
KeywordsRobotTerrainBrakeSimulationEngineeringInclined planeComputer scienceControl theory (sociology)Artificial intelligenceControl (management)Automotive engineeringMechanical engineeringGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.222
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it