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Record W2121033336 · doi:10.1109/iros.1998.724611

3D in-hand manipulation planning

2002· article· en· W2121033336 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
TopicRobot Manipulation and Learning
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRedundancy (engineering)Computer scienceMotion planningControl reconfigurationGRASPConfiguration spaceTrajectoryPiecewiseObject (grammar)PlannerRobotHumanoid robotArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

This paper describes a global motion planner for dextrous manipulation of 3D objects by a multifingered robotic hand. We focus on the reconfiguration problem: find a feasible quasi-static trajectory (motions and contact forces) that moves a hand-object system from an initial grasp to a final desired configuration of the object. The planner is designed as a two-level process: a global level that expands a tree of sub-goals in the configuration space of the object, and a local level that searches for feasible quasi-static trajectories of the entire manipulation system between adjacent sub-goals. A key feature of the planner is that it exploits the redundancy of the system by using, in a complementary way, different canonical manipulation modes and tackles the high dimensionality of the solution space (configuration and control spaces) by making use, instantaneously, of a random search over it. The planner is applied in simulation for achieving several nontrivial reconfiguration tasks for (piecewise-) smooth convex objects demonstrating the promise of our approach.

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 categoriesInsufficient 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 score0.596
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 integrity0.0000.000
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 head0.048
GPT teacher head0.234
Teacher spread0.186 · 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

Citations8
Published2002
Admission routes1
Has abstractyes

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