TRANSFORMATION FOR COVARIATE SHIFT OF GRASP NEURAL NETWORKS

A covariate shift generally refers to the change of the distribution of the input data (e.g., noise distribution) between the training and inference regimes. Such covariate shifts can degrade the performance grasping neural networks, and thus robotic grasping operations. As described herein, an outp...

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Bibliographische Detailangaben
Hauptverfasser:	XIA, Wei Xi, UGALDE DIAZ, Ines, SATHYA NARAYANAN, Gokul Narayanan, SOLOWJOW, Eugen, APARICIO OJEA, Juan L, TAMASKAR, Shashank, SHAHAPURKAR, Yash, WEN, Chengtao, CLAUSSEN, Heiko
Format:	Patent
Sprache:	eng ; fre ; ger
Schlagworte:	CALCULATING CHAMBERS PROVIDED WITH MANIPULATION DEVICES COMPUTING COUNTING HAND TOOLS MANIPULATORS PERFORMING OPERATIONS PHYSICS PORTABLE POWER-DRIVEN TOOLS TRANSPORTING
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Beschreibung
Zusammenfassung:	A covariate shift generally refers to the change of the distribution of the input data (e.g., noise distribution) between the training and inference regimes. Such covariate shifts can degrade the performance grasping neural networks, and thus robotic grasping operations. As described herein, an output of a grasp neural network can be transformed, so as to determine appropriate locations on a given object for a robot or autonomous machine to grasp.