As robots become smarter and more ubiquitous, optimizing the power consumption of intelligent compute becomes imperative towards ensuring the sustainability of technological advancements. Neuromorphic computing hardware makes use of biologically inspired neural architectures to achieve energy and latency improvements compared to conventional von Neumann computing architecture. Applying these benefits to robots has been demonstrated in several works in the field of neurorobotics, typically on relatively simple control tasks. Here, we introduce an example of neuromorphic computing applied to the real-world industrial task of object insertion. We trained a spiking neural network (SNN) to perform force-torque feedback control using a reinforcement learning approach in simulation. We then ported the SNN to the Intel neuromorphic research chip Loihi interfaced with a KUKA robotic arm. At inference time we show latency competitive with current CPU/GPU architectures, two orders of magnitude less energy usage in comparison to traditional low-energy edge-hardware. We offer this example as a proof of concept implementation of a neuromoprhic controller in real-world robotic setting, highlighting the benefits of neuromorphic hardware for the development of intelligent controllers for robots.

翻译：随着机器人日益智能化和普及化，优化智能计算的功耗对于确保技术进步的可持续性至关重要。神经形态计算硬件采用仿生神经架构，相较于传统冯·诺依曼计算架构，在能量效率和延迟性能上具有显著优势。已有若干神经机器人学领域的研究展示了该项技术在机器人中的应用，但现有工作多集中于相对简单的控制任务。本文提出将神经形态计算应用于零件插入这一真实工业任务的典型案例。我们采用强化学习方法在仿真环境中训练脉冲神经网络(SNN)执行力-力矩反馈控制，随后将该SNN移植至英特尔神经形态研究芯片Loihi，并与库卡机械臂进行接口集成。推理阶段测试表明，该系统的延迟性能可与当前CPU/GPU架构相媲美，而能耗相较传统低功耗边缘硬件降低两个数量级。本研究作为神经形态控制器在真实工业机器人场景的概念验证实现，突显了神经形态硬件在开发智能机器人控制器方面的优势。