  As robots aspire for long-term autonomous operations in complex dynamic
environments, the ability to reliably take mission-critical decisions in
ambiguous situations becomes critical. This motivates the need to build systems
that have situational awareness to assess how qualified they are at that moment
to make a decision. We call this self-evaluating capability as introspection.
In this paper, we take a small step in this direction and propose a generic
framework for introspective behavior in perception systems. Our goal is to
learn a model to reliably predict failures in a given system, with respect to a
task, directly from input sensor data. We present this in the context of
vision-based autonomous MAV flight in outdoor natural environments, and show
that it effectively handles uncertain situations.
