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Record W2774098503 · doi:10.1109/iros.2017.8206382

Control strategy and implementation for a humanoid robot pushing a heavy load on a rolling cart

2017· article· en· W2774098503 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHumanoid robotController (irrigation)CartTorqueRobotComputer scienceControl theory (sociology)Control engineeringHeavy loadRobot controlSimulationControl (management)Mobile robotEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we introduce a control strategy aimed at generating a stable walking pattern for a humanoid pushing a heavy load on a cart. In contrast to previous approaches that rely on force/torque sensors to measure the interaction between the robot and the pushed object, we present a simple model-based controller that can be implemented on most robots due to its computationally efficient design. Every aspect of the controller design is covered, from the formulation and validation of the dynamic model, to the implementation and validation on an actual humanoid robot. The experimental results show that the controller can efficiently make a NAO humanoid transport, in a stable way, the equivalent of its own weight on a rolling cart.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.422

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 head0.020
GPT teacher head0.289
Teacher spread0.269 · 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

Quick stats

Citations14
Published2017
Admission routes1
Has abstractyes

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