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Reinforcement learning has shown a wide usage in robotics tasks, such asinsertion and grasping. However, without a practical sim2real strategy, thepolicy trained in simulation could fail on the real task. There are also wideresearches in the sim2real strategies, but most of those methods rely on heavyimage rendering, domain randomization training, or tuning. In this work, wesolve the insertion task using a pure visual reinforcement learning solutionwith minimum infrastructure requirement. We also propose a novel sim2realstrategy, Real2Sim, which provides a novel and easier solution in policyadaptation. We discuss the advantage of Real2Sim compared with Sim2Real.

Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation