Policy Transfer with Strategy Optimization

TL;DR

This paper introduces a novel transfer learning method for robotic control policies that learns a family of policies in simulation and searches for the best one in the real environment, bypassing the need for precise system identification.

Contribution

It proposes a strategy optimization approach that leverages domain randomization to transfer control policies to unknown environments without explicit system identification.

Findings

Outperforms robust and adaptive policies in large modeling errors

Effective across five different simulated robotic tasks

Enables transfer to environments with significant discrepancies

Abstract

Computer simulation provides an automatic and safe way for training robotic control policies to achieve complex tasks such as locomotion. However, a policy trained in simulation usually does not transfer directly to the real hardware due to the differences between the two environments. Transfer learning using domain randomization is a promising approach, but it usually assumes that the target environment is close to the distribution of the training environments, thus relying heavily on accurate system identification. In this paper, we present a different approach that leverages domain randomization for transferring control policies to unknown environments. The key idea that, instead of learning a single policy in the simulation, we simultaneously learn a family of policies that exhibit different behaviors. When tested in the target environment, we directly search for the best policy in the family based on the task performance, without the need to identify the dynamic parameters. We evaluate our method on five simulated robotic control problems with different discrepancies in the training and testing environment and demonstrate that our method can overcome larger modeling errors compared to training a robust policy or an adaptive policy.