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The intelligent biped robot AIRO: System overview and omnidirectional walking method design

2022· article· en· W4317383007 on OpenAlexaff
Yun Liu, Anhuan Xie, Shiqiang Zhu, Jason Gu

Bibliographic record

Venue2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) · 2022
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOmnidirectional antennaRobotInverted pendulumControl theory (sociology)Control engineeringComputer scienceSimulationEngineeringControl (management)Artificial intelligenceNonlinear systemPhysics

Abstract

fetched live from OpenAlex

In order to verify the omnidirectional walking theory and to meet the requirements of rapid prototyping and economy, a bipedal robot system AIRO based on commercial servo motor-driven joints is designed and implemented. Compared with other motor-driven biped robots of the same size, the structure body of AIRO is lighter, and the leg mass distribution is more in line with the principle of the linear inverted pendulum model. Firstly, the overall system composition of the robot is introduced, including the mechanical structure part and the control system part; Then, based on the linear inverted pendulum model principle and the input speed command of the system, the omnidirectional walking planning and control of the robot is realized in combination with the current state of the robot; Finally, a successful omnidirectional walking experiment was carried out on AIRO to verify the effectiveness of the biped robot motion system.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.058
GPT teacher head0.287
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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