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

arXiv: https://arxiv.org/abs/1903.01066 · HTML: https://mlsanity.com/paper/1903.01066/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

## Built on by (most cited first, 30 of 39)

- [SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning](https://mlsanity.com/paper/2401.16013/) (2024)
- [Multi-Stage Cable Routing through Hierarchical Imitation Learning](https://mlsanity.com/paper/2307.08927/) (2023)
- [TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction](https://mlsanity.com/paper/2405.10315/) (2024)
- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning](https://mlsanity.com/paper/2412.09858/) (2024)
- [Offline Meta-Reinforcement Learning for Industrial Insertion](https://mlsanity.com/paper/2110.04276/) (2021)
- [RGL-NET: A Recurrent Graph Learning framework for Progressive Part Assembly](https://mlsanity.com/paper/2107.12859/) (2021)
- [Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study](https://mlsanity.com/paper/2103.11512/) (2021)
- [Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation](https://mlsanity.com/paper/2008.07682/) (2020)
- [On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer](https://mlsanity.com/paper/2312.03673/) (2023)
- [TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation](https://mlsanity.com/paper/2409.11047/) (2024)
- [Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learning](https://mlsanity.com/paper/2103.06484/) (2021)
- [SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering](https://mlsanity.com/paper/2210.15185/) (2022)
- [Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing](https://mlsanity.com/paper/2402.17464/) (2024)
- [Continuous Control with Coarse-to-fine Reinforcement Learning](https://mlsanity.com/paper/2407.07787/) (2024)
- [RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment](https://mlsanity.com/paper/2112.10143/) (2021)
- [Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision](https://mlsanity.com/paper/2210.15206/) (2022)
- [Robotic Table Tennis: A Case Study into a High Speed Learning System](https://mlsanity.com/paper/2309.03315/) (2023)
- [Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation](https://mlsanity.com/paper/2504.06961/) (2025)
- [GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2404.06645/) (2024)
- [OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation](https://mlsanity.com/paper/2110.00704/) (2021)
- [FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2509.17053/) (2025)
- [Learning to Design and Construct Bridge without Blueprint](https://mlsanity.com/paper/2108.02439/) (2021)
- [SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training](https://mlsanity.com/paper/2507.04452/) (2025)
- [Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories](https://mlsanity.com/paper/2212.01498/) (2022)
- [Combining Learning from Demonstration with Learning by Exploration to Facilitate Contact-Rich Tasks](https://mlsanity.com/paper/2103.05904/) (2021)
- [Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resets](https://mlsanity.com/paper/2204.05509/) (2022)
- [A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation](https://mlsanity.com/paper/2207.13438/) (2022)
- [Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot](https://mlsanity.com/paper/2311.07245/) (2023)
- [PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation](https://mlsanity.com/paper/2312.02531/) (2023)

## 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.