  Despite the progress in legged robotic locomotion, autonomous navigation in
unknown environments remains an open problem. Ideally, the navigation system
utilizes the full potential of the robots' locomotion capabilities while
operating within safety limits under uncertainty. The robot must sense and
analyze the traversability of the surrounding terrain, which depends on the
hardware, locomotion control, and terrain properties. It may contain
information about the risk, energy, or time consumption needed to traverse the
terrain. To avoid hand-crafted traversability cost functions we propose to
collect traversability information about the robot and locomotion policy by
simulating the traversal over randomly generated terrains using a physics
simulator. Thousand of robots are simulated in parallel controlled by the same
locomotion policy used in reality to acquire 57 years of real-world locomotion
experience equivalent. For deployment on the real robot, a sparse convolutional
network is trained to predict the simulated traversability cost, which is
tailored to the deployed locomotion policy, from an entirely geometric
representation of the environment in the form of a 3D voxel-occupancy map. This
representation avoids the need for commonly used elevation maps, which are
error-prone in the presence of overhanging obstacles and multi-floor or
low-ceiling scenarios. The effectiveness of the proposed traversability
prediction network is demonstrated for path planning for the legged robot
ANYmal in various indoor and natural environments.
