Most certainly because they've shown that imitation learning worked inspire of randomness inherent in a natural settings (colours, visuals, obstacles etc).
Imitation learning from human demonstrations has shown impressive performance in robotics. However, most results focus on table-top manipulation, lacking the mobility and dexterity necessary for generally useful tasks. In this work, we develop a system for imitating mobile manipulation tasks that are bimanual and require whole-body control.
Most certainly because they've shown that imitation learning worked inspire of randomness inherent in a natural settings (colours, visuals, obstacles etc).