We present the first demonstration of a fully spiking actor-critic neural network policy, trained via Proximal Policy Optimization (PPO), for continuous control of an agile high-speed quadcopter in a gate-based navigation task. The spiking neural network (SNN) controller employs Leaky Integrate-and-Fire neurons with surrogate gradient training and spike-rate decoding over multiple integration cycles, and it is benchmarked against a comparable artificial neural network (ANN) controller in both simulation and real-world flight tests. Results show that despite being trained to the same reward level, the SNN achieves superior performance in simulation, achieving higher episode rewards, greater robustness and reduced crash rate. Additionally, in 12-second real-world trials, the SNN outperforms the ANN, attaining a higher average reward (70.63 vs 59.77), greater mean velocity (7.94 vs 6.99 m/s), and more gates cleared (46.33 vs 40.67). An analysis of the spike integration cycle count reveals a clear trade-off: lower cycle counts (fewer integration steps per control update) reduce control output resolution and hinder learning, whereas higher cycle counts improve smoothness but increase inference latency. Moderate cycle counts (5 or 8) provide the best balance, yielding high rewards, smoother outputs, and low execution time overhead. These findings represent a key step forward for neuromorphic control in embedded autonomous systems, demonstrating that SNN-based policies can outperform conventional ANN controllers in high-speed, agile robotic tasks.

Quadrotors have continuously leveraged the use of artificial intelligence for navigation and decision-making. Moreover, neuromorphic computing, specifically Spiking Neural Networks (SNNs), is considered as an energy-efficient solution during inference. The current study will analyse the effects of implementing SNNs for mimicking energy optimal guidance and control. To achieve this, population encoding is used and an equivalent of 7-8 spiking neurons per conventional neuron is found to preserve most of the information. The equivalent controller prefers fast adaptation which requires small spiking threshold values and minimal reliance on past information. To improve the controller performance, dataset selection is of utmost importance with a careful trade-off between excessive race track customisation and generalisability being required. The results show that learning is feasible and SNN performance approaches conventional state-of-the-art models trained with multi-layer perceptrons. The current analysis represent an important step towards the rapid guidance and control of ultra-small energy efficient quadrotors.