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Naturally Compliant Dexterous Anthropomorphic Hand via Novel Modular Soft-Rigid Hybrid Robotics Approach: Design Rationale, Assembly Methods, and Evaluation

2023· article· en· W4388624080 on OpenAlexaff
Peter S. Lee, Cameron Sjaarda, Rhys Cornelious, Run Ze Gao, Kaichao Lu, Carolyn L. Ren

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Waterloo
FundersScience and Engineering Research Council
KeywordsModular designFlexibility (engineering)Rigidity (electromagnetism)Computer scienceActuatorRobotSoft roboticsRoboticsArtificial intelligenceInterface (matter)Constraint (computer-aided design)Human–computer interactionControl engineeringSimulationEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

In this paper, we propose a modular soft-rigid hybrid (MSRH) approach for designing a highly anthropomorphous and dexterous robotic hand. This MSRH approach allows the robotic hand to possess an inherently soft and compliant interface suitable for pHRI while maintaining the structural rigidity of the robot with the usage of rigid skeletons. We share the details of the design rationale, fabrication and assembly methods, and evaluation of the first prototype. Even though the presented prototype is scaled to be 125% of the average human hand, the mechanical components only weigh less than 450 g. The modular design approach allows the MSRH hand to have low manufacturing costs and a short lead time. Creation of the presented prototype costs less than ${\$}$100 CAD and can be built in three days from scratch with two-person labour. The usage of pneumatic soft robotic actuators also provides flexibility over choosing the number of controlled DOF and joint coupling. This hence provides a new angle to tackle spatial constraint that normally arises in robotic hand design with increased anthropomorphism. Lastly, the dexterity of the proposed hand is demonstrated by evaluating the hand using taxonomies highly relevant to replicating tasks humans perform daily.

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 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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.479
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.088
GPT teacher head0.325
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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