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Visual Servoing in Autoencoder Latent Space: Supplementary material

Samuel Felton 1, 2 Pascal Brault 2 Elisa Fromont 1, 3, 4, 5 Eric Marchand 2, 3, 4
1 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
2 RAINBOW - Sensor-based and interactive robotics
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : This document provides additional information about [1], describing further experiments with Autoencoderbased visual servoing (AEVS), a method for visual servoing in the latent space of an autoencoder. Multiple experiments are described, both in a simulated environment and on a real robot. In this document, we first evaluate the impact of the network specification and dataset sizes on the visual servoing results. Then we conduct experiments to ascertain the applicability and transferability of the method to complex scenes.
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Preprints, Working Papers, ...
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https://hal.inria.fr/hal-03448667
Contributor : Eric Marchand Connect in order to contact the contributor
Submitted on : Thursday, November 25, 2021 - 11:55:33 AM
Last modification on : Saturday, November 27, 2021 - 3:12:38 AM

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RA_L_addendum-2.pdf
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  • HAL Id : hal-03448667, version 1

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Samuel Felton, Pascal Brault, Elisa Fromont, Eric Marchand. Visual Servoing in Autoencoder Latent Space: Supplementary material. 2021. ⟨hal-03448667⟩

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