Classifying Obstacles and Exploiting Knowledge about Classes for Efficient Humanoid Navigation

In this paper, we propose a new approach to

humanoid navigation through cluttered environments that ex-

ploits knowledge about different obstacle classes and selects

appropriate robot actions. To classify objects from RGB images

and decide whether an obstacle can be overcome by the robot

with a corresponding action, e.g., by pushing or carrying it

aside or stepping over or onto it, we train a convolutional

neural network (CNN). Based on the associated action costs, we

compute a cost grid of the environment on which a 2D path can

be efficiently planned. This path encodes the necessary actions

that need to be carried out to reach the goal. We implemented

our framework in ROS and tested it in various scenarios with

a Nao robot. As the experiments demonstrate, using the CNN

the robot can robustly classify the observed obstacles into

the different classes and exploit this information to efficiently

compute solution paths. Our system finds paths also through

regions where traditional planning methods are not able to

calculate a solution or require substantially more time.