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Unsupervised Learning of Visual 3D Keypoints for Control
[article]
2021
arXiv
pre-print
Learning sensorimotor control policies from high-dimensional images crucially relies on the quality of the underlying visual representations. Prior works show that structured latent space such as visual keypoints often outperforms unstructured representations for robotic control. However, most of these representations, whether structured or unstructured are learned in a 2D space even though the control tasks are usually performed in a 3D environment. In this work, we propose a framework to
arXiv:2106.07643v1
fatcat:5vmjphexcbeydjixga245sr764