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We propose Automatic Curricula via Expert Demonstrations (ACED), areinforcement learning (RL) approach that combines the ideas of imitationlearning and curriculum learning in order to solve challenging roboticmanipulation tasks with sparse reward functions. Curriculum learning solvescomplicated RL tasks by introducing a sequence of auxiliary tasks withincreasing difficulty, yet how to automatically design effective andgeneralizable curricula remains a challenging research problem. ACED extractscurricula from a small amount of expert demonstration trajectories by dividingdemonstrations into sections and initializing training episodes to statessampled from different sections of demonstrations. Through moving the resetstates from the end to the beginning of demonstrations as the learning agentimproves its performance, ACED not only learns challenging manipulation taskswith unseen initializations and goals, but also discovers novel solutions thatare distinct from the demonstrations. In addition, ACED can be naturallycombined with other imitation learning methods to utilize expert demonstrationsin a more efficient manner, and we show that a combination of ACED withbehavior cloning allows pick-and-place tasks to be learned with as few as 1demonstration and block stacking tasks to be learned with 20 demonstrations.