# Learning to Centralize Dual-Arm Assembly

arXiv: https://arxiv.org/abs/2110.04003 · HTML: https://mlsanity.com/paper/2110.04003/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 8 Oct 2021.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [Active Domain Randomization](https://mlsanity.com/paper/1904.04762/) (2019)
- [The Ingredients of Real-World Robotic Reinforcement Learning](https://mlsanity.com/paper/2004.12570/) (2020)
- [An empirical investigation of the challenges of real-world reinforcement learning](https://mlsanity.com/paper/2003.11881/) (2020)
- [Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks](https://mlsanity.com/paper/1906.08880/) (2019)
- [Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards](https://mlsanity.com/paper/1906.05841/) (2019)
- [Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation](https://mlsanity.com/paper/1904.06703/) (2019)
- [Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2008.10224/) (2020)
- [Hierarchical Reinforcement Learning for Quadruped Locomotion](https://mlsanity.com/paper/1905.08926/) (2019)
- [A Comparison of Action Spaces for Learning Manipulation Tasks](https://mlsanity.com/paper/1908.08659/) (2019)
- [Improving dual-arm assembly by master-slave compliance](https://mlsanity.com/paper/1902.07007/) (2019)
- [Learning and Generalisation of Primitives Skills Towards Robust Dual-arm Manipulation](https://mlsanity.com/paper/1904.01568/) (2019)

## Built on by (most cited first, 9 of 9)

- [On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer](https://mlsanity.com/paper/2312.03673/) (2023)
- [The Reality Gap in Robotics: Challenges, Solutions, and Best Practices](https://mlsanity.com/paper/2510.20808/) (2025)
- [Accelerating Model-Based Reinforcement Learning with State-Space World Models](https://mlsanity.com/paper/2502.20168/) (2025)
- [APEX: Ambidextrous Dual-Arm Robotic Manipulation Using Collision-Free Generative Diffusion Models](https://mlsanity.com/paper/2404.02284/) (2024)
- [CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces](https://mlsanity.com/paper/2211.15824/) (2022)
- [LIMT: Language-Informed Multi-Task Visual World Models](https://mlsanity.com/paper/2407.13466/) (2024)
- [The Shortcomings of Force-from-Motion in Robot Learning](https://mlsanity.com/paper/2407.02904/) (2024)
- [Dual-Arm Adversarial Robot Learning](https://mlsanity.com/paper/2110.08066/) (2021)
- [A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation](https://mlsanity.com/paper/2303.15262/) (2023)

## Abstract (arXiv)

Robotic manipulators are widely used in modern manufacturing processes. However, their deployment in unstructured environments remains an open problem. To deal with the variety, complexity, and uncertainty of real-world manipulation tasks, it is essential to develop a flexible framework with reduced assumptions on the environment characteristics. In recent years, reinforcement learning (RL) has shown great results for single-arm robotic manipulation. However, research focusing on dual-arm manipulation is still rare. From a classical control perspective, solving such tasks often involves complex modeling of interactions between two manipulators and the objects encountered in the tasks, as well as the two robots coupling at a control level. Instead, in this work, we explore the applicability of model-free RL to dual-arm assembly. As we aim to contribute towards an approach that is not limited to dual-arm assembly, but dual-arm manipulation in general, we keep modeling efforts at a minimum. Hence, to avoid modeling the interaction between the two robots and the used assembly tools, we present a modular approach with two decentralized single-arm controllers which are coupled using a single centralized learned policy. We reduce modeling effort to a minimum by using sparse rewards only. Our architecture enables successful assembly and simple transfer from simulation to the real world. We demonstrate the effectiveness of the framework on dual-arm peg-in-hole and analyze sample efficiency and success rates for different action spaces. Moreover, we compare results on different clearances and showcase disturbance recovery and robustness, when dealing with position uncertainties. Finally we zero-shot transfer policies trained in simulation to the real world and evaluate their performance.