Improving Autonomous Robotic Navigation Using Imitation Learning.

ABSTRACT: Autonomous navigation to a specified waypoint is traditionally accomplished with a layered stack of global path planning and local motion planning modules that generate feasible and obstacle-free trajectories. While these modules can be modified to meet task-specific constraints and user preferences, current modification procedures require substantial effort on the part of an expert roboticist with a great deal of technical training. In this paper, we simplify this process by inserting a Machine Learning module between the global path planning and local motion planning modules of an off-the shelf navigation stack. This model can be trained with human demonstrations of the preferred navigation behavior, using a training procedure based on Behavioral Cloning, allowing for an intuitive modifi

SUBMITTER: Cesar-Tondreau B 

PROVIDER: S-EPMC8204187 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature