Adaptive sampling is claimed to expand what reinforcement-learning robots can do

The author credits earlier adaptive-curriculum work and the open-ended reinforcement-learning community for the underlying idea. They say their simple adaptive-sampling approach scales with compute, requires no hand engineering and learns robot policies entirely through reinforcement learning, without imitation data. They claim those policies can solve tasks previously beyond reinforcement learning.