Hierarchical Learning for Modular Robots
This addresses the challenge of reconfigurability for modular robots, though it appears incremental as it builds on existing hierarchical methods.
The paper tackles the problem of enabling modular robots to learn multiple tasks simultaneously by proposing a hierarchical approach, demonstrating that a trained neural network can select appropriate motor primitives and configurations to achieve targets, with evaluation on 3DoF and 4DoF configurations showing successful transfer to real robots.
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.