Do Not Make the Same Mistakes Again and Again: Learning Local Recovery Policies for Navigation from Human Demonstrations
In this letter, we present a human-in-the-loop learning framework for mobile robots to generate effective local policies in order to recover from navigation failures in long-term autonomy. We present an analysis of failure and recovery cases derived from long-term autonomous operation of a mobile robot, and propose a two-layer learning framework that allows to detect and recover from such navigation failures. Employing a learning by demonstration approach, our framework can incrementally learn to autonomously recover from situations it initially needs humans to help with.