论文标题
SIM2REAL转移用于增强学习而无需动态随机性
Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization
论文作者
论文摘要
在这项工作中，我们展示了如何在笛卡尔空间中使用联合和笛卡尔限制下使用的操作空间控制框架（OSC）。因此，我们的方法能够通过可调节的自由度进行快速学习，而我们能够传输策略，而无需对KUKA LBR IIWA peg内孔任务进行其他动态随机化。在模拟开始学习之前，我们执行系统识别，以尽可能与真实机器人的动力学对齐模拟环境。将约束添加到OSC控制器中，使我们能够以安全的方式在真实的机器人上学习，或者学习灵活的目标条件策略，可以轻松地将其从模拟转移到真实的机器人。
In this work we show how to use the Operational Space Control framework (OSC) under joint and cartesian constraints for reinforcement learning in cartesian space. Our method is therefore able to learn fast and with adjustable degrees of freedom, while we are able to transfer policies without additional dynamics randomizations on a KUKA LBR iiwa peg in-hole task. Before learning in simulation starts, we perform a system identification for aligning the simulation environment as far as possible with the dynamics of a real robot. Adding constraints to the OSC controller allows us to learn in a safe way on the real robot or to learn a flexible, goal conditioned policy that can be easily transferred from simulation to the real robot.
