Distilled Domain Randomization

TL;DR

This paper introduces DiDoR, a method combining reinforcement learning with policy distillation to effectively transfer controllers from simulation to real robots without additional real-world data.

Contribution

It proposes a novel algorithm that distills policies from multiple simulated domains into a single robust policy for sim-to-real transfer.

Findings

DiDoR achieves comparable or better performance than baselines in various experiments.

The method does not increase memory or computation time for deployment.

DiDoR effectively bridges the reality gap in robot control policies.

Abstract

Deep reinforcement learning is an effective tool to learn robot control policies from scratch. However, these methods are notorious for the enormous amount of required training data which is prohibitively expensive to collect on real robots. A highly popular alternative is to learn from simulations, allowing to generate the data much faster, safer, and cheaper. Since all simulators are mere models of reality, there are inevitable differences between the simulated and the real data, often referenced as the 'reality gap'. To bridge this gap, many approaches learn one policy from a distribution over simulators. In this paper, we propose to combine reinforcement learning from randomized physics simulations with policy distillation. Our algorithm, called Distilled Domain Randomization (DiDoR), distills so-called teacher policies, which are experts on domains that have been sampled initially, into a student policy that is later deployed. This way, DiDoR learns controllers which transfer directly from simulation to reality, i.e., without requiring data from the target domain. We compare DiDoR against three baselines in three sim-to-sim as well as two sim-to-real experiments. Our results show that the target domain performance of policies trained with DiDoR is en par or better than the baselines'. Moreover, our approach neither increases the required memory capacity nor the time to compute an action, which may well be a point of failure for successfully deploying the learned controller.