Navigation by Imitation in a Pedestrian-Rich Environment

Understand

Deep neural networks trained on demonstrations of human actions give robot the ability to perform self-driving on the road.

• However, navigation in a pedestrian-rich environment, such as a campus setup, is still challenging---one needs to take frequent interventions to the robot and take control over the robot from early steps leading to a mistake.
• An arduous burden is, hence, placed on the learning framework design and data acquisition.
• In this paper, we propose a new learning-from-intervention Dataset Aggregation (DAgger) algorithm to overcome the limitations brought by applying imitation learning to navigation in the pedestrian-rich environment.