# Deep Model Predictive Variable Impedance Control

arXiv: https://arxiv.org/abs/2209.09614 · HTML: https://mlsanity.com/paper/2209.09614/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2022.

## Built on by (most cited first, 2 of 2)

- [DA-VIL: Adaptive Dual-Arm Manipulation with Reinforcement Learning and Variable Impedance Control](https://mlsanity.com/paper/2410.19712/) (2024)
- [A Contact Model based on Denoising Diffusion to Learn Variable Impedance Control for Contact-rich Manipulation](https://mlsanity.com/paper/2403.13221/) (2024)

## Abstract (arXiv)

The capability to adapt compliance by varying muscle stiffness is crucial for dexterous manipulation skills in humans. Incorporating compliance in robot motor control is crucial to performing real-world force interaction tasks with human-level dexterity. This work presents a Deep Model Predictive Variable Impedance Controller for compliant robotic manipulation which combines Variable Impedance Control with Model Predictive Control (MPC). A generalized Cartesian impedance model of a robot manipulator is learned using an exploration strategy maximizing the information gain. This model is used within an MPC framework to adapt the impedance parameters of a low-level variable impedance controller to achieve the desired compliance behavior for different manipulation tasks without any retraining or finetuning. The deep Model Predictive Variable Impedance Control approach is evaluated using a Franka Emika Panda robotic manipulator operating on different manipulation tasks in simulations and real experiments. The proposed approach was compared with model-free and model-based reinforcement approaches in variable impedance control for transferability between tasks and performance.