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/
@article{arxiv.1910.07972,
title = {Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control},
author = {Lukas Hermann and Max Argus and Andreas Eitel and Artemij Amiranashvili and Wolfram Burgard and Thomas Brox},
journal= {arXiv preprint arXiv:1910.07972},
year = {2020}
}
Comments
Accepted at the 2020 IEEE International Conference on Robotics and Automation (ICRA). Project page see https://lmb.informatik.uni-freiburg.de/projects/curriculum/