Current low latency neuromorphic processing systems, and future ones based on ultra-low power mixed-signal circuits in advanced technology nodes and memristive nano-scale devices, hold great potential for developing autonomous artificial agents. However, the variable nature and low precision of the underlying hardware substrate pose severe challenges for robust and reliable performance. To address these challenges, we adopt hardware-friendly processing strategies based on brain-inspired computational primitives, such as triplet spike-timing dependent plasticity, basal ganglia inspired disinhibition, and cooperative-competitive networks. We demonstrate this approach by presenting an example of robust real-time motor control using a hardware spiking neural network implemented on a mixed-signal neuromorphic processor, trained to learn the inverse kinematics of a 2-joint robotic arm. The final system is able to perform low-latency control robustly and reliably using noisy silicon neurons. The spiking neural network, trained to control two joints of the iCub robot arm simulator, performs a continuous target reaching task with 97.93% accuracy, 33.96 ms network latency, 102.1 ms system latency and with an estimated power consumption of 26.92 µW. This work provides insights into how specific computational primitives used by real neural systems can be applied to neuromorphic computing for solving real-world engineering tasks. It represents a milestone in the design of end-to-end spiking robotic control systems, relying on event-driven sensory encoding, neuromorphic processing, and spiking motor control.