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Record W3175546856 · doi:10.15607/rss.2021.xvii.060

GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels

2021· preprint· en· W3175546856 on OpenAlexafffund
Dylan Turpin, Liquan Wang, Stavros Tsogkas, Sven Dickinson, Animesh Garg

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsVector InstituteUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsAffordanceComputer scienceLeverage (statistics)GRASPHuman–computer interactionOracleTask (project management)Representation (politics)Object (grammar)Set (abstract data type)Artificial intelligenceMachine learningEngineering

Abstract

fetched live from OpenAlex

across three manipulation tasks: by interacting with procedurally-generated tools Discover tool a ordances hooking, from RGBD observations of unknown objects reaching hammering & match expected task semantics across A ordance predictions and are similar to those of a human labeller, e.g., for hammering. human labeller Train an a ordance model and predict distributions over pairs of keypoints to argmax to detect sparse keypoints representing tool geometry by learning from the contact data of sampled trajectories.

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)0.000
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 score0.908
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)0.0070.000

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 head0.044
GPT teacher head0.258
Teacher spread0.214 · 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.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2021
Admission routes2
Has abstractyes

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