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Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly

arXiv 1903.01066 ↗First posted 4 Mar 2019cs.ROcited by 33

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Abstract (arXiv)

Precise robotic manipulation skills are desirable in many industrial settings, reinforcement learning (RL) methods hold the promise of acquiring these skills autonomously. In this paper, we explicitly consider incorporating operational space force/torque information into reinforcement learning; this is motivated by humans heuristically mapping perceived forces to control actions, which results in completing high-precision tasks in a fairly easy manner. Our approach combines RL with force/torque information by incorporating a proper operational space force controller; where we also exploit different ablations on processing this information. Moreover, we propose a neural network architecture that generalizes to reasonable variations of the environment. We evaluate our method on the open-source Siemens Robot Learning Challenge, which requires precise and delicate force-controlled behavior to assemble a tight-fit gear wheel set.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Mar 2019.

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Citing papers per year: 2020: 1; 2021: 9; 2022: 6; 2023: 5; 2024: 9; 2025: 3.

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  1. 2024SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learningcited by 113
  2. 2023Multi-Stage Cable Routing through Hierarchical Imitation Learningcited by 59
  3. 2024TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correctioncited by 50
  4. 2022Factory: Fast Contact for Robotic Assemblycited by 49
  5. 2024RLDG: Robotic Generalist Policy Distillation via Reinforcement Learningcited by 42
  6. 2021Offline Meta-Reinforcement Learning for Industrial Insertioncited by 37
  7. 2021RGL-NET: A Recurrent Graph Learning framework for Progressive Part Assemblycited by 35
  8. 2021Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Studycited by 24
  9. 2024TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulationcited by 23
  10. 2020Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulationcited by 22
  11. 2022SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Renderingcited by 22
  12. 2021Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learningcited by 20
21 more
  1. 2023On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfercited by 20
  2. 2024Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passingcited by 17
  3. 2024Continuous Control with Coarse-to-fine Reinforcement Learningcited by 15
  4. 2021RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environmentcited by 13
  5. 2023Robotic Table Tennis: A Case Study into a High Speed Learning Systemcited by 12
  6. 2025Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulationcited by 12
  7. 2022Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Visioncited by 11
  8. 2024GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Taskscited by 9
  9. 2021OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulationcited by 7
  10. 2021Learning to Design and Construct Bridge without Blueprintcited by 6
  11. 2025FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Taskscited by 6
  12. 2022Policy Learning for Active Target Tracking over Continuous SE(3) Trajectoriescited by 3
  13. 2022Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resetscited by 2
  14. 2021Combining Learning from Demonstration with Learning by Exploration to Facilitate Contact-Rich Taskscited by 1
  15. 2022A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulationcited by 1
  16. 2023Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robotcited by 1
  17. 2024The Shortcomings of Force-from-Motion in Robot Learningcited by 1
  18. 2025CHEQ-ing the Box: Safe Variable Impedance Learning for Robotic Polishingcited by 1
  19. 2021Progressive extension of reinforcement learning action dimension for asymmetric assembly taskscited by 0
  20. 2023PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptationcited by 0
  21. 2024On the role of Artificial Intelligence methods in modern force-controlled manufacturing robotic taskscited by 0