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Human-Humanoid Robot Cooperative Load Transportation: Model-based Control Approach

2022· article· en· W4312645764 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

Venue2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 2022
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsHumanoid robotRobotObserver (physics)Task (project management)Computer scienceTorqueSimulationRobot controlHuman–computer interactionMobile robotControl engineeringArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

In order to properly integrate humanoid robots in real-life situations, they must be able to collaborate with humans in completing tasks. One of these tasks is the cooperative transportation of a heavy object, which has been widely studied in the humanoids literature. However, the proposed methods rely heavily on six-axis force/torque (F/T) sensors at the wrists, which medium-sized or even some full-sized humanoid robots do not have. This paper proposes an observer to overcome the lack of F/T sensors. The observer is then coupled with a simplified dynamic model of the transportation task allowing the humanoid robot to carry out the task in a stable way. The method is tested in simulation using a humanoid robot that does not have F/T sensors, a NAO robot, to demonstrate its performance. These tests pointed out that the proposed method successfully estimated the interaction forces while generating stable walking patterns.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.980
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.263
Teacher spread0.222 · 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