On the Performance of Online Adaptation of Robots Controlled by Nanowire Networks

In order to complete useful tasks in complex and changing contexts, robots need to be able to adapt their behavior or actions. This requires the ability to find effective approaches to the situation at hand, and to maintain them until they keep being effective. The final goal can be summarized by the will of maintaining a high performance during the whole life of the robot, regardless of the changes that may intervene during its activity. In this work, we evaluate by the point of view of the resulting life-long performance two methodologies for the adaptation of robots controlled by immutable network-based control systems: the Nanowire Networks. We demonstrate that modifying the best found solution leads to constant improvement and to overall better cumulative performance. Complementarily, we show that a less constrained approach simplifies the exploration of different behaviors but reduces life-long performance. Finally, we confirm previous results suggesting the potential of using this novel neuromorphic device (i.e., the Nanowire Network) for the control of robots.