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We propose Adaptive Curriculum Generation from Demonstrations (ACGD) forreinforcement learning in the presence of sparse rewards. Rather than designingshaped reward functions, ACGD adaptively sets the appropriate task difficultyfor the learner by controlling where to sample from the demonstrationtrajectories and which set of simulation parameters to use. We show thattraining vision-based control policies in simulation while gradually increasingthe difficulty of the task via ACGD improves the policy transfer to the realworld. The degree of domain randomization is also gradually increased throughthe task difficulty. We demonstrate zero-shot transfer for two real-worldmanipulation tasks: pick-and-stow and block stacking. A video showing theresults can be found athttps://lmb.informatik.uni-freiburg.de/projects/curriculum/

Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control