Title: Hierarchical Learning for Modular Robots,
Abstract: We argue that hierarchical methods can become the key for modular robots
achieving reconfigurability. We present a hierarchical approach for modular
robots that allows a robot to simultaneously learn multiple tasks. Our
evaluation results present an environment composed of two different modular
robot configurations, namely 3 degrees-of-freedom (DoF) and 4DoF with two
corresponding targets. During the training, we switch between configurations
and targets aiming to evaluate the possibility of training a neural network
that is able to select appropriate motor primitives and robot configuration to
achieve the target. The trained neural network is then transferred and executed
on a real robot with 3DoF and 4DoF configurations. We demonstrate how this
technique generalizes to robots with different configurations and tasks.