  The third generation of artificial intelligence (AI) introduced by
neuromorphic computing is revolutionizing the way robots and autonomous systems
can sense the world, process the information, and interact with their
environment. The promises of high flexibility, energy efficiency, and
robustness of neuromorphic systems is widely supported by software tools for
simulating spiking neural networks, and hardware integration (neuromorphic
processors). Yet, while efforts have been made on neuromorphic vision
(event-based cameras), it is worth noting that most of the sensors available
for robotics remain inherently incompatible with neuromorphic computing, where
information is encoded into spikes. To facilitate the use of traditional
sensors, we need to convert the output signals into streams of spikes, i.e., a
series of events (+1, -1) along with their corresponding timestamps. In this
paper, we propose a review of the coding algorithms from a robotics perspective
and further supported by a benchmark to assess their performance. We also
introduce a ROS (Robot Operating System) toolbox to encode and decode input
signals coming from any type of sensor available on a robot. This initiative is
meant to stimulate and facilitate robotic integration of neuromorphic AI, with
the opportunity to adapt traditional off-the-shelf sensors to spiking neural
nets within one of the most powerful robotic tools, ROS.
