Self-supervised Body Image Acquisition Using a Deep Neural Network for Sensorimotor Prediction
This addresses the challenge of body image acquisition for robotics and AI agents, but it is incremental as it builds on existing self-supervised and predictability concepts.
The paper tackles the problem of enabling a naive agent to acquire its own body image through self-supervised learning by leveraging the predictability of sensorimotor experiences, and it demonstrates that a deep neural network can automatically isolate the robot's arm from the environment with evaluated quality.
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.