Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation
This work addresses the sim2real gap for robotics insertion tasks, offering a more efficient alternative to existing methods.
The paper tackles the problem of transferring reinforcement learning policies from simulation to real-world robotics insertion tasks by proposing a novel Real2Sim adaptation strategy, achieving successful policy transfer with minimal infrastructure.
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.