Online vs. Offline Adaptive Domain Randomization Benchmark

Physics simulators have shown great promise for conveniently learning reinforcement learning policies in safe, unconstrained environments. However, transferring the acquired knowledge to the real world can be challenging due to the reality gap. To this end, several methods have been recently proposed to automatically tune simulator parameters with posterior distributions given real data, for use with domain randomization at training time. These approaches have been shown to work for various robotic tasks under different settings and assumptions. Nevertheless, existing literature lacks a thorough comparison of existing adaptive domain randomization methods with respect to transfer performance and real-data efficiency. In this work, we present an open benchmark for both offline and online methods (SimOpt, BayRn, DROID, DROPO), to shed light on which are most suitable for each setting and task at hand. We found that online methods are limited by the quality of the currently learned policy for the next iteration, while offline methods may sometimes fail when replaying trajectories in simulation with open-loop commands.

Authored by Gabriele Tiboni, Karol Arndt, Giuseppe Averta, Ville Kyrki, Tatiana Tammasi.

Overview of tasks, methods and dynamics settings used in this benchmark.
*The source domain is under-modeled in the dynamics space.


#1 Offline methods are more data efficient Offline methods often reached the same long-term performance as online methods with as little as a single real trajectory. This wasn't necessarily true for DROID due to finding #4. #2 Bayesian Optimization does not scale to high-dimensional tasks While successfully solving the Hopper task, Bayesian optimization (BayRN) would not scale to high-dimensional inference tasks with only 5 iterations. Such tasks include Half Cheetah, Walker2D and Humanoid which require inference of posterior distributions over 8, 13 and 30 dynamics parameters respectively. #3 Online methods may fail unexpectedly due to bad intermediate policies Online methods such as BayRN and SimOpt may fail unexpectedly when policies learned at intermediate iterations fail to transfer to the target domain and do not collect informative data for inferring the desired dynamics parameters. This phenomenon occurred more frequently in complex tasks, e.g. Humanoid. #4 Offline methods may fail when replaying offline commands in simulation Offline methods would sometimes produce meaningless results when real-world commands are replayed in simulation (open loop) on missmatched dynamics, leading to divergent trajectories.
Resetting the simulator state to each individual starting state when replaying offline trajectories—as in DROPO—seemed to solve the issue completely.


  author="Tiboni, Gabriele and Arndt, Karol and Averta, Giuseppe and Kyrki, Ville and Tommasi, Tatiana",
  editor="Borja, Pablo and Della Santina, Cosimo and Peternel, Luka and Torta, Elena",
  title="Online vs. Offline Adaptive Domain Randomization Benchmark",
  booktitle="Human-Friendly Robotics 2022",
  publisher="Springer International Publishing",