Unsupervised Feature Learning for Manipulation with Contrastive Domain Randomization

Robotic tasks such as manipulation with visual inputs require image features that capture the physical properties of the scene, e.g., the position and configuration of objects. Recently, it has been suggested to learn such features in an unsupervised manner from simulated, self-supervised, robot int...

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Veröffentlicht in:	arXiv.org 2021-06
Hauptverfasser:	Rabinovitz, Carmel, Grupen, Niko, Aviv Tamar
Format:	Artikel
Sprache:	eng
Schlagworte:	Domains Image manipulation Physical properties Randomization Simulation Simulators Unsupervised learning
Online-Zugang:	Volltext
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