  Learning-based navigation systems are widely used in autonomous applications,
such as robotics, unmanned vehicles and drones. Specialized hardware
accelerators have been proposed for high-performance and energy-efficiency for
such navigational tasks. However, transient and permanent faults are increasing
in hardware systems and can catastrophically violate tasks safety. Meanwhile,
traditional redundancy-based protection methods are challenging to deploy on
resource-constrained edge applications. In this paper, we experimentally
evaluate the resilience of navigation systems with respect to algorithms, fault
models and data types from both RL training and inference. We further propose
two efficient fault mitigation techniques that achieve 2x success rate and 39%
quality-of-flight improvement in learning-based navigation systems.
