论文标题
REAL2SIM或SIM2REAL：使用深度强化学习和Real2SIM策略适应的机器人技术插入
Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation
论文作者
论文摘要
强化学习表明在机器人技术任务（例如插入和抓紧）中使用了广泛的用法。但是，如果没有实用的SIM2REAL策略，对模拟进行培训的政策可能会失败。 SIM2Real策略中也有广泛的研究，但是大多数方法都依赖于重型图像渲染，域随机训练或调整。在这项工作中，我们使用最低基础架构要求使用纯粹的视觉增强学习解决方案来解决插入任务。我们还提出了一种新颖的SIM2REAL策略Real2SIM，该策略在政策适应方面提供了一种新颖，更轻松的解决方案。我们讨论了与Sim2Real相比，Real2SIM的优势。
Reinforcement learning has shown a wide usage in robotics tasks, such as insertion and grasping. However, without a practical sim2real strategy, the policy trained in simulation could fail on the real task. There are also wide researches in the sim2real strategies, but most of those methods rely on heavy image rendering, domain randomization training, or tuning. In this work, we solve the insertion task using a pure visual reinforcement learning solution with minimum infrastructure requirement. We also propose a novel sim2real strategy, Real2Sim, which provides a novel and easier solution in policy adaptation. We discuss the advantage of Real2Sim compared with Sim2Real.
