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25 April 2024 |
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Article overview
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Self-supervised Body Image Acquisition Using a Deep Neural Network for Sensorimotor Prediction | | Alban Laflaquière
; Verena V. Hafner
; | | Date: |
3 Jun 2019 | | Abstract: | This work investigates how a naive agent can acquire its own body image in a
self-supervised way, based on the predictability of its sensorimotor
experience. Our working hypothesis is that, due to its temporal stability, an
agent’s body produces more consistent sensory experiences than the environment,
which exhibits a greater variability. Given its motor experience, an agent can
thus reliably predict what appearance its body should have. This intrinsic
predictability can be used to automatically isolate the body image from the
rest of the environment. We propose a two-branches deconvolutional neural
network to predict the visual sensory state associated with an input motor
state, as well as the prediction error associated with this input. We train the
network on a dataset of first-person images collected with a simulated Pepper
robot, and show how the network outputs can be used to automatically isolate
its visible arm from the rest of the environment. Finally, the quality of the
body image produced by the network is evaluated. | | Source: | arXiv, 1906.0825 | | Services: | Forum | Review | PDF | Favorites | |
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