BADGR successfully reached its goal while avoiding collisions and bumpy terrain, unlike a geometry-based policy. Source: Greg Kahn, BAIR
A geometric approach to mobile robot navigation and obstacle avoidance may be sufficient for environments such as warehouses, but it might not be enough for dynamic settings outdoors. Researchers at the University of California, Berkeley, said they have developed BADGR, “an end-to-end, learning-based mobile robot navigation system that can be trained with self-supervised, off-policy data gathered in real-world environments, without any simulation or human supervision.”
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