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Action Jets: Learning the Local Shape of the Action-conditioned Data Manifold
Author1, Author2, Author3, Author4, Author5
action-conditioned modelsmanifold learningplanningefficiency
2610.19007
Builder Relevance
2h ago80%
Abstract
This paper presents a method for efficiently evaluating action candidates in high-dimensional predictive models by estimating the local shape of the action-conditioned data manifold.
Reality Card
Core Claim
The Action Jet network allows planners to evaluate multiple action candidates over short horizons with over 300x speedup in inference time compared to traditional methods.
Method / Result
Scoring sixteen candidates from a single extraction is over 300x faster than with either source generator.
Limitations
The method's performance may vary based on the quality of the data used for training the Action Jet network.
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