  We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for
reinforcement learning in the presence of sparse rewards. Rather than designing
shaped reward functions, ACGD adaptively sets the appropriate task difficulty
for the learner by controlling where to sample from the demonstration
trajectories and which set of simulation parameters to use. We show that
training vision-based control policies in simulation while gradually increasing
the difficulty of the task via ACGD improves the policy transfer to the real
world. The degree of domain randomization is also gradually increased through
the task difficulty. We demonstrate zero-shot transfer for two real-world
manipulation tasks: pick-and-stow and block stacking. A video showing the
results can be found at
https://lmb.informatik.uni-freiburg.de/projects/curriculum/
