  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. Our new learning algorithm
implements an error backtrack function that is able to effectively learn from
expert interventions. Combining our new learning algorithm with deep
convolutional neural networks and a hierarchically-nested policy-selection
mechanism, we show that our robot is able to map pixels direct to control
commands and navigate successfully in real world without explicitly modeling
the pedestrian behaviors or the world model.
