mlsanityData to 7 Oct 2026

Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks

arXiv 2004.14404 ↗First posted 29 Apr 2020cs.LG cs.ROcited by 28

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Abstract (arXiv)

Robotic insertion tasks are characterized by contact and friction mechanics, making them challenging for conventional feedback control methods due to unmodeled physical effects. Reinforcement learning (RL) is a promising approach for learning control policies in such settings. However, RL can be unsafe during exploration and might require a large amount of real-world training data, which is expensive to collect. In this paper, we study how to use meta-reinforcement learning to solve the bulk of the problem in simulation by solving a family of simulated industrial insertion tasks and then adapt policies quickly in the real world. We demonstrate our approach by training an agent to successfully perform challenging real-world insertion tasks using less than 20 trials of real-world experience. Videos and other material are available at https://pearl-insertion.github.io/

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 9; 2023: 5; 2024: 6; 2025: 1; 2026: 1.

28 papers.

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  1. 2024TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correctioncited by 50
  2. 2022Factory: Fast Contact for Robotic Assemblycited by 49
  3. 2021Offline Meta-Reinforcement Learning for Industrial Insertioncited by 37
  4. 2024JUICER: Data-Efficient Imitation Learning for Robotic Assemblycited by 36
  5. 2023MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulationcited by 30
  6. 2021Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Studycited by 24
  7. 2024TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulationcited by 23
  8. 2025ObjectVLA: End-to-End Open-World Object Manipulation Without Demonstrationcited by 20
  9. 2021Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Taskscited by 18
  10. 2022Robust Policy Learning over Multiple Uncertainty Setscited by 16
  11. 2022Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Visioncited by 11
  12. 2021Coarse-to-Fine for Sim-to-Real: Sub-Millimetre Precision Across Wide Task Spacescited by 9
16 more
  1. 2021Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertaintycited by 6
  2. 2022Giving Feedback on Interactive Student Programs with Meta-Explorationcited by 6
  3. 2022Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learningcited by 5
  4. 2022Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptationcited by 3
  5. 2024Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfercited by 3
  6. 2022Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Studycited by 2
  7. 2023Zero-Shot Transfer of Haptics-Based Object Insertion Policiescited by 2
  8. 2023On First-Order Meta-Reinforcement Learning with Moreau Envelopescited by 1
  9. 2024Deep Reinforcement Learning for Traveling Purchaser Problemscited by 1
  10. 2024Automating Deformable Gasket Assemblycited by 1
  11. 2021Analyzing the Hidden Activations of Deep Policy Networks: Why Representation Matterscited by 0
  12. 2022Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holescited by 0
  13. 2022Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learningcited by 0
  14. 2023Knolling Bot: Teaching Robots the Human Notion of Tidinesscited by 0
  15. 2023PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptationcited by 0
  16. 2026Few-Shot Learning for Dynamic Operations of Automated Electric Taxi Fleets under Evolving Charging Infrastructure: A Meta-Deep Reinforcement Learning Approachcited by 0