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Record W4386902776 · doi:10.1109/mra.2023.3310858

Synthesis, Design, and Experimental Validation of an Agile Wrist for Enhanced Grasping and Manipulation in Cluttered Environments: An Experimental Evaluation With a Challenging Pick-and-Place Task

2023· article· en· W4386902776 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

VenueIEEE Robotics & Automation Magazine · 2023
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAgile software developmentTask (project management)SMT placement equipmentComputer scienceTrajectoryRobotArtificial intelligenceComputer visionWristHuman–computer interactionSimulationMotion (physics)EngineeringSystems engineeringSoftware engineering

Abstract

fetched live from OpenAlex

In many practical cases, such as in logistics applications, grasping and manipulation of objects must be carried out in confined spaces with obstacles. Limited room is then available to maneuver, and robotic devices can lack dexterity or be impaired by their size when performing in such cluttered environments. In this article, we address this issue by proposing a novel agile wrist based on a rolling joint with a ± 180° range of motion (ROM). This wrist is designed to maneuver in confined spaces and in particular access hard-to-reach areas through entry apertures, while allowing the use of constraining grasping methods, such as scooping. The architecture of this device is discussed, and a prototype is built. A dedicated trajectory planning strategy based on a virtual center of motion is proposed, and an experimental evaluation with a challenging pick-and-place task is carried out.

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: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.791

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)0.0000.000

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 head0.037
GPT teacher head0.287
Teacher spread0.250 · 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