Transferring spatial perception between robots operating in a shared workspace

We use a Katana robotic arm to teach an iCub humanoid robot how to perceive the location of the objects it sees. To do this, the Katana positions an object within the shared workspace, and tells the iCub where it has placed it. While the iCub moves it observes the object, and a neural network then learns how to relate its pose and visual inputs to the object location. We show that satisfactory results can be obtained for localisation even in scenarios where the kinematic model is imprecise or not available. Furthermore, we demonstrate that this task can be accomplished safely. For this task we extend our collision avoidance software for the iCub to prevent collisions between multiple, independently controlled, heterogeneous robots in the same workspace.