Improving Autonomous Robotic Navigation Using Imitation Learning

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Frontiers Media

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 modification of the navigation policy by non-technical users to suit task-specific constraints. We find that our approach can successfully adapt a robot’s navigation behavior to become more like that of a demonstrator. Moreover, for a fixed amount of demonstration data, we find that the proposed technique compares favorably to recent baselines with respect to both navigation success rate and trajectory similarity to the demonstrator.

autonomous navigation, learning from demonstration, imitation learning, human in the loop, robot learning and behavior adaptation
Cèsar-Tondreau B, Warnell G, Stump E, Kochersberger K and Waytowich NR (2021) Improving Autonomous Robotic Navigation Using Imitation Learning. Front. Robot. AI 8:627730. doi: 10.3389/frobt.2021.627730