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.

中文翻译：

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机器人技术中神经形态感应的工具箱

神经形态计算技术引入的第三代人工智能（AI）正在彻底改变机器人和自治系统感知世界，处理信息以及与环境互动的方式。用于模拟尖峰神经网络和硬件集成（神经形态处理器）的软件工具广泛支持神经形态系统的高度灵活性，能效和鲁棒性。然而，尽管已经在神经形态视觉（基于事件的相机）方面做出了努力，但值得注意的是，大多数可用于机器人技术的传感器仍然固有地与神经形态计算不兼容，在神经形态计算中，信息被编码为尖峰。为了方便使用传统传感器，我们需要将输出信号转换为尖峰流，即一系列事件（+1，-1）及其相应的时间戳。在本文中，我们从机器人技术的角度提出了编码算法的综述，并在基准的支持下进一步评估了它们的性能。我们还引入了ROS（机器人操作系统）工具箱，以对来自机器人上任何类型的传感器的输入信号进行编码和解码。该计划旨在刺激和促进神经形态AI的机器人集成，并有机会使传统的现成传感器适应最强大的机器人工具之一ROS中的尖刺神经网络。我们还引入了ROS（机器人操作系统）工具箱，以对来自机器人上任何类型的传感器的输入信号进行编码和解码。该计划旨在刺激和促进神经形态AI的机器人集成，并有机会使传统的现成传感器适应最强大的机器人工具之一ROS中的尖刺神经网络。我们还引入了ROS（机器人操作系统）工具箱，以对来自机器人上任何类型的传感器的输入信号进行编码和解码。该计划旨在刺激和促进神经形态AI的机器人集成，并有机会使传统的现成传感器适应最强大的机器人工具之一ROS中的尖刺神经网络。