Introspective perception: Learning to predict failures in vision systems
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.
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)