Artificial neural networks for spatial perception: Towards visual object localisation in humanoid robots
Jürgen Leitner, Simon Peter Harding, Mikhail Frank, Alexander Förster, Jürgen Schmidhuber · 2013
In this paper, we present our on-going research to allow humanoid robots to learn spatial perception. We are using artificial neural networks (ANN) to estimate the location of objects in the robot's environment. The method is using only the visual inputs and the joint encoder readings, no camera calibration and information is necessary, nor is a kinematic model. We find that these ANNs can be trained to allow spatial perception in Cartesian (3D) coordinates. These lightweight networks are providing estimates that are comparable to current state of the art approaches and can easily be used together with existing operational space controllers.