Learning in dynamic environments with Ensemble Selection for autonomous outdoor robot navigation

Abstract

Autonomous robot navigation in unstructured outdoor environments is a challenging area of active research. The navigation task requires identifying safe, traversable paths which allow the robot to progress toward a goal while avoiding obstacles. Machine learning techniques - in particular, classifier ensembles - are well adapted to this task, accomplishing near-to-far learning by augmenting near-field stereo readings in order to identify safe terrain and obstacles in the far field. Composition of the ensemble and subsequent combination of model outputs in this dynamic problem domain remain open questions. Recently, Ensemble selection has been proposed as a mechanism for selecting and combining models from an existing model library and shown to perform well in static domains. We propose the adaptation of this technique to the time-evolving data associated with the outdoor robot navigation domain. Important research questions as to the composition of the model library, as well as how to combine selected modelspsila outputs, are addressed in a two-factor experimental evaluation. We evaluate the performance of our technique on six fully labeled datasets, and show that our technique outperforms memoryless baseline techniques that do not leverage past experience.