The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. 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.
翻译:深层次学习的兴起导致了机器人研究的范式转变,有利于需要大量数据的方法。 在物理平台上生成这类数据集的成本太高,令人望而却步。 因此,在数据生成既快又便宜的模拟中学习最先进的方法,然后将知识传授给真正的机器人(sim-to- real ) 。 尽管越来越现实,但所有模拟器都是基于模型的构造,因此必然不完美。 这就提出了如何修改模拟器以促进学习机器人控制政策并克服模拟与现实之间的不匹配的问题,这通常被称为“真实差距 ” 。 我们提供了对机器人模拟到真实研究的全面审查, 重点是名为“ 随机化” 的技术, 这是一种从随机化模拟中学习的方法。