mlsanityData to 7 Oct 2026

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

arXiv 2206.02679 ↗First posted 6 Jun 2022cs.RO cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 6 Jun 2022.

Builds on

14 of 19 references.

References · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2015Trust Region Policy Optimizationcited by 2,503
  5. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  6. 2019Solving Rubik's Cube with a Robot Handcited by 699
  7. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 302
  8. 2020RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Realcited by 89
  9. 2017Deep Reinforcement Learning for High Precision Assembly Taskscited by 37
  10. 2018A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learningcited by 35
  11. 2020Meta-Reinforcement Learning for Robotic Industrial Insertion Taskscited by 28
  12. 2020Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approachcited by 25
2 more
  1. 2019Affordance Learning for End-to-End Visuomotor Robot Controlcited by 18
  2. 2019Adversarial Feature Training for Generalizable Robotic Visuomotor Controlcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 1.

3 papers.

  1. 2023Addressing Weak Decision Boundaries in Image Classification by Leveraging Web Search and Generative Modelscited by 1
  2. 2023Extreme Image Transformations Facilitate Robust Latent Object Representationscited by 0
  3. 2026Sim-to-Real Transfer via a Style-Identified Cycle Consistent Generative Adversarial Network: Zero-Shot Deployment on Robotic Manipulators through Visual Domain Adaptationcited by 0