# Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2008.10224 · HTML: https://mlsanity.com/paper/2008.10224/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 29 of 29)

- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [JUICER: Data-Efficient Imitation Learning for Robotic Assembly](https://mlsanity.com/paper/2404.03729/) (2024)
- [TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation](https://mlsanity.com/paper/2409.11047/) (2024)
- [Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks](https://mlsanity.com/paper/2106.14070/) (2021)
- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [Touch begins where vision ends: Generalizable policies for contact-rich manipulation](https://mlsanity.com/paper/2506.13762/) (2025)
- [Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System](https://mlsanity.com/paper/2406.14990/) (2024)
- [Coarse-to-Fine for Sim-to-Real: Sub-Millimetre Precision Across Wide Task Spaces](https://mlsanity.com/paper/2105.11283/) (2021)
- [Learning to Centralize Dual-Arm Assembly](https://mlsanity.com/paper/2110.04003/) (2021)
- [Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist](https://mlsanity.com/paper/2402.18002/) (2024)
- [GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2404.06645/) (2024)
- [SliceIt! -- A Dual Simulator Framework for Learning Robot Food Slicing](https://mlsanity.com/paper/2404.02569/) (2024)
- [Visual Spatial Attention and Proprioceptive Data-Driven Reinforcement Learning for Robust Peg-in-Hole Task Under Variable Conditions](https://mlsanity.com/paper/2312.16438/) (2023)
- [Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation](https://mlsanity.com/paper/2206.02679/) (2022)
- [SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks](https://mlsanity.com/paper/2509.13949/) (2025)
- [Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study](https://mlsanity.com/paper/2204.12844/) (2022)
- [Zero-Shot Transfer of Haptics-Based Object Insertion Policies](https://mlsanity.com/paper/2301.12587/) (2023)
- [PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation](https://mlsanity.com/paper/2312.02531/) (2023)
- [Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space](https://mlsanity.com/paper/2405.01134/) (2024)
- [A General Peg-in-Hole Assembly Policy Based on Domain Randomized Reinforcement Learning](https://mlsanity.com/paper/2504.04148/) (2025)
- [Constraint-Grounded Reinforcement Learning for Variable Impedance Control in Contact-Rich Robotic Insertion](https://mlsanity.com/paper/2609.13516/) (2026)
- [Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes](https://mlsanity.com/paper/2205.04297/) (2022)
- [Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning](https://mlsanity.com/paper/2205.05963/) (2022)
- [Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing](https://mlsanity.com/paper/2206.14122/) (2022)
- [A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation](https://mlsanity.com/paper/2303.15262/) (2023)
- [Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm](https://mlsanity.com/paper/2308.14652/) (2023)
- [Imitation learning for sim-to-real transfer of robotic cutting policies based on residual Gaussian process disturbance force model](https://mlsanity.com/paper/2311.04096/) (2023)
- [Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations](https://mlsanity.com/paper/2508.01082/) (2025)
- [End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting](https://mlsanity.com/paper/2601.20846/) (2026)

## Abstract (arXiv)

Industrial robot manipulators are playing a more significant role in modern manufacturing industries. Though peg-in-hole assembly is a common industrial task which has been extensively researched, safely solving complex high precision assembly in an unstructured environment remains an open problem. Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially when using position-controlled manipulators. The main contribution of this work is a learning-based method to solve peg-in-hole tasks with position uncertainty of the hole. We proposed the use of an off-policy model-free reinforcement learning method and bootstrap the training speed by using several transfer learning techniques (sim2real) and domain randomization. Our proposed learning framework for position-controlled robots was extensively evaluated on contact-rich insertion tasks on a variety of environments.