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Deep Reinforcement Learning for High Precision Assembly Tasks

arXiv 1708.04033 ↗First posted 14 Aug 2017cs.RO cs.AIcited by 37

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Abstract (arXiv)

High precision assembly of mechanical parts requires accuracy exceeding the robot precision. Conventional part mating methods used in the current manufacturing requires tedious tuning of numerous parameters before deployment. We show how the robot can successfully perform a tight clearance peg-in-hole task through training a recurrent neural network with reinforcement learning. In addition to saving the manual effort, the proposed technique also shows robustness against position and angle errors for the peg-in-hole task. The neural network learns to take the optimal action by observing the robot sensors to estimate the system state. The advantages of our proposed method is validated experimentally on a 7-axis articulated robot arm.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Aug 2017.

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Citing papers per year: 2019: 1; 2020: 5; 2021: 9; 2022: 10; 2023: 4; 2024: 4; 2025: 1; 2026: 3.

37 papers.

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  1. 2022Factory: Fast Contact for Robotic Assemblycited by 49
  2. 2021Offline Meta-Reinforcement Learning for Industrial Insertioncited by 37
  3. 2021Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Studycited by 24
  4. 2024TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulationcited by 23
  5. 2020Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulationcited by 22
  6. 2021Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulationcited by 16
  7. 2021InsertionNet -- A Scalable Solution for Insertioncited by 15
  8. 2021Learning Variable Impedance Control via Inverse Reinforcement Learning for Force-Related Taskscited by 10
  9. 2022InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Inputcited by 9
  10. 2020Understanding Multi-Modal Perception Using Behavioral Cloning for Peg-In-a-Hole Insertion Taskscited by 8
  11. 2022Break and Make: Interactive Structural Understanding Using LEGO Brickscited by 8
  12. 2021Benchmarking Off-The-Shelf Solutions to Robotic Assembly Taskscited by 6
25 more
  1. 2021Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertaintycited by 6
  2. 2019Contact Skill Imitation Learning for Robot-Independent Assembly Programmingcited by 4
  3. 2020Imitation Learning for High Precision Peg-in-Hole Taskscited by 3
  4. 2022Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptationcited by 3
  5. 2022Renaissance Robot: Optimal Transport Policy Fusion for Learning Diverse Skillscited by 2
  6. 2023Zero-Shot Transfer of Haptics-Based Object Insertion Policiescited by 2
  7. 2023Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approachcited by 2
  8. 2023Visual Spatial Attention and Proprioceptive Data-Driven Reinforcement Learning for Robust Peg-in-Hole Task Under Variable Conditionscited by 2
  9. 2024The State of Robot Motion Generationcited by 2
  10. 2021Combining Learning from Demonstration with Learning by Exploration to Facilitate Contact-Rich Taskscited by 1
  11. 2022Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scrapingcited by 1
  12. 2022Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Controlcited by 1
  13. 2023Learning Human-Inspired Force Strategies for Robotic Assemblycited by 1
  14. 2024Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Spacecited by 1
  15. 2026Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulationcited by 1
  16. 2020Multi-Instance Aware Localization for End-to-End Imitation Learningcited by 0
  17. 2020Stochastic Action Prediction for Imitation Learningcited by 0
  18. 2021Progressive extension of reinforcement learning action dimension for asymmetric assembly taskscited by 0
  19. 2022Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Controlcited by 0
  20. 2022Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holescited by 0
  21. 2022Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learningcited by 0
  22. 2024A Peg-in-hole Task Strategy for Holes in Concretecited by 0
  23. 2025AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Usecited by 0
  24. 2026SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policycited by 0
  25. 2026Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning frameworkcited by 0