# Learning Variable Impedance Control for Contact Sensitive Tasks

arXiv: https://arxiv.org/abs/1907.07500 · HTML: https://mlsanity.com/paper/1907.07500/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 16 of 16)

- [LASER: Learning a Latent Action Space for Efficient Reinforcement Learning](https://mlsanity.com/paper/2103.15793/) (2021)
- [On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer](https://mlsanity.com/paper/2312.03673/) (2023)
- [Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization](https://mlsanity.com/paper/2107.06629/) (2021)
- [CORN: Contact-based Object Representation for Nonprehensile Manipulation of General Unseen Objects](https://mlsanity.com/paper/2403.10760/) (2024)
- [Learning to Pick at Non-Zero-Velocity from Interactive Demonstrations](https://mlsanity.com/paper/2110.04534/) (2021)
- [CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces](https://mlsanity.com/paper/2211.15824/) (2022)
- [Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space](https://mlsanity.com/paper/2110.09904/) (2021)
- [Impedance Primitive-augmented Hierarchical Reinforcement Learning for Sequential Tasks](https://mlsanity.com/paper/2508.19607/) (2025)
- [Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning](https://mlsanity.com/paper/2203.12048/) (2022)
- [Investigating the Impact of Action Representations in Policy Gradient Algorithms](https://mlsanity.com/paper/2309.06921/) (2023)
- [CHEQ-ing the Box: Safe Variable Impedance Learning for Robotic Polishing](https://mlsanity.com/paper/2501.07985/) (2025)
- [Variable Stiffness for Robust Locomotion through Reinforcement Learning](https://mlsanity.com/paper/2502.09436/) (2025)
- [Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing](https://mlsanity.com/paper/2206.14122/) (2022)
- [Learning Variable Impedance Skills from Demonstrations with Passivity Guarantee](https://mlsanity.com/paper/2306.11308/) (2023)
- [A Coordinated Dual-Arm Framework for Delicate Snap-Fit Assemblies](https://mlsanity.com/paper/2511.18153/) (2025)
- [Behavior Cloning Under PD Control: A Finite-Horizon Theory of Gain-Dependent Error Amplification](https://mlsanity.com/paper/2604.14484/) (2026)

## Abstract (arXiv)

Reinforcement learning algorithms have shown great success in solving different problems ranging from playing video games to robotics. However, they struggle to solve delicate robotic problems, especially those involving contact interactions. Though in principle a policy directly outputting joint torques should be able to learn to perform these tasks, in practice we see that it has difficulty to robustly solve the problem without any given structure in the action space. In this paper, we investigate how the choice of action space can give robust performance in presence of contact uncertainties. We propose learning a policy giving as output impedance and desired position in joint space and compare the performance of that approach to torque and position control under different contact uncertainties. Furthermore, we propose an additional reward term designed to regularize these variable impedance control policies, giving them interpretability and facilitating their transfer to real systems. We present extensive experiments in simulation of both floating and fixed-base systems in tasks involving contact uncertainties, as well as results for running the learned policies on a real system.