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US 2024/0408758 A1Published application
Generative adversarial networks for detecting erroneous results
AssigneeTRACLabs, Inc.
InventorsMICHAEL W. LANIGHAN, OSCAR M. YOUNGQUIST
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Abstract
Systems and methods leveraging machine learning models to provide error detection in robotic systems. Generative adversarial networks are utilized to provide detection of off-nominal behaviors that may be flagged for user review or cause termination of any pending operations. A positive manifold may be used to reduce overhead in building error detection systems as this reduces the amount of training data required.
Claims13
A system for detecting off-nominal behavior comprising:
- a trained manifold developed with a target behavior dataset;
- an encoder configured to receive at least one initial data element, said encoder being further configured to generate a lower dimensional data element from said at least one initial data element;
- a generator configured to generate a reconstructed data element by mapping said lower dimensional data element onto said trained manifold;
- and
- a classifier configured to measure a divergence between said initial data element and said reconstructed data element wherein a divergence beyond a pre-determined threshold is indicative of off-nominal behavior.
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A system for detecting off-nominal behavior comprising:
- a robotic control platform configured to observe and control operations of a robotic system,
- said robotic control platform comprising:
- a user interface configured to allow a user to remotely direct operations of said robotic system;
- a trained manifold developed with a target behavior dataset;
- an encoder configured to receive at least one initial data element, wherein said user interface is further configured to allow said user to cause said at least one initial data element to be communicated to said encoder, said encoder being further configured to generate a lower dimensional data element from said at least one initial data element;
- a generator configured to generate a reconstructed data element by mapping said lower dimensional data element onto said trained manifold;
- and
- a classifier configured to measure a divergence between said initial data element and said reconstructed data element wherein a divergence beyond a pre-determined threshold is indicative of off-nominal behavior, said user interface being further configured to notify said user upon said classifier detecting said off-nominal behavior.
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Dependencies are inferred from the claim text.