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Record W1970308219 · doi:10.5555/2447556.2447673

Taking your robot for a walk: force-guiding a mobile robot using compliant arms

2013· article· en· W1970308219 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

VenueHuman-Robot Interaction · 2013
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHolonomicRobotMobile robotComputer scienceRobot controlBang-bang robotOmnidirectional antennaSimulationTeleoperationImpedance controlTask (project management)Artificial intelligenceEngineeringHuman–computer interactionComputer vision

Abstract

fetched live from OpenAlex

Guiding a mobile robot by the hand would make a simple and natural interface. This requires the ability to sense forces applied on the robot from direct physical contacts, and to translate these forces into motion commands. This paper presents a joint-space impedance control approach that does so by perceiving forces applied on compliant arms, making the robot react as a real-life physical object to a user pulling and pushing on one or both of its arms. By independently controlling stiffness in specific degrees-of-freedom, our approach allows the general position of the arms to change to the preferences of the person interacting with it, a capability that is not possible using a strictly position-based control approach. A test case with 15 volunteers was conducted on IRL-1, an omnidirectional, non-holonomic mobile robot, to study and fine-tune our approach in an unconstrained guiding task, making IRL-1 go in and out of a room through a doorway.

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 score0.750
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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.217
GPT teacher head0.365
Teacher spread0.148 · 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