# Self-supervised Body Image Acquisition Using a Deep Neural Network for Sensorimotor Prediction

## TL;DR

This paper presents a self-supervised neural network approach enabling a robot to autonomously acquire its own visual body image by predicting sensorimotor experiences, effectively isolating its body from the environment.

## Contribution

It introduces a novel two-branch deconvolutional neural network that predicts visual sensory states and errors for body image acquisition in a self-supervised manner.

## Key findings

- Network successfully isolates the robot's arm from the environment.
- The approach leverages sensorimotor predictability to distinguish body parts.
- The method is validated on a simulated Pepper robot dataset.

## 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.