Event based sensors and neural processing architectures represent a promising technology for implementing low power and low latency robotic control systems. However, the implementation of robust and reliable control architectures using neuromorphic devices is challenging, due to their limited precision and variable nature of their underlying computing elements. In this paper we demonstrate robust obstacle avoidance and target acquisition behaviors in a compact mobile platform controlled by a neuromorphic sensory-processing system and validate its performance in a number of robotic experiments.