@article {pub4516,
title = {Data-efficient Domain Randomization with Bayesian Optimization},
author = {Fabio Muratore AND Christian Eilers AND Michael Gienger AND Jan Peters},
year = {2021},
month = {May},
abstract = {REFERENCE PROPOSALS: pub-4266 and pub-4169
When learning policies for robot control, the required real-world data is typically prohibitively expensive to acquire, so learning in simulation is a popular strategy. Unfortunately, such polices are often not transferable to the real world due to a mismatch between the simulation and reality, called {\textquoteleft}reality gap{\textquoteright}. Domain randomization methods tackle this problem by randomizing the physics simulator (source domain) during training according to a distribution over domain parameters in order to obtain more robust policies that are able to overcome the reality gap. Most domain randomization approaches sample the domain parameters from a fixed distribution. This solution is suboptimal in the context of sim-to-real transferability, since it yields policies that have been trained without explicitly optimizing for the reward on the real system (target domain). Additionally, a fixed distribution assumes there is prior knowledge about the uncertainty over the domain parameters. Thus, we propose Bayesian Domain Randomization (BayRn), a black-box sim-to-real algorithm that solves tasks efficiently by adapting the domain parameter distribution during learning given sparse data from the real-world target domain. BayRn uses Bayesian optimization to search the space of source domain distribution parameters such that this leads to a policy which maximizes the real-word objective, allowing for adaptive distributions during policy optimization. We experimentally validate the proposed approach in sim-to-sim as well as in sim-to-real settings, comparing against three baseline methods on two robotic tasks. Our results show that BayRn is able to perform sim-to-real transfer, while significantly reducing the required prior knowledge.},
publisher = {IEEE},
journal = {IEEE Robotics and Automation Letters (RA-L)},
editor = {Dana Kulic}
}