ランダム化シミュレーションからのロボット学習：レビュー
Robot Learning from Randomized Simulations: A Review
シミュレーションと現実のギャップを埋めるため、シミュレーションのパラメータをランダム化して学習するドメインランダム化を中心に、sim-to-real研究を包括的にレビューした論文。
著者: Fabio Muratore, Fabio Ramos, Greg Turk, Wenhao Yu, Michael Gienger, Jan Peters
分類: cs.RO, cs.LG
原文アブストラクト
The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. Unfortunately, it is prohibitively expensive to generate such data sets on a physical platform. Therefore, state-of-the-art approaches learn in simulation where data generation is fast as well as inexpensive and subsequently transfer the knowledge to the real robot (sim-to-real). Despite becoming increasingly realistic, all simulators are by construction based on models, hence inevitably imperfect. This raises the question of how simulators can be modified to facilitate learning robot control policies and overcome the mismatch between simulation and reality, often called the 'reality gap'. We provide a comprehensive review of sim-to-real research for robotics, focusing on a technique named 'domain randomization' which is a method for learning from randomized simulations.
