Learning variable impedance control for contact sensitive tasks

Miroslav Bogdanovic, Majid Khadiv, Ludovic Righetti

Research output: Contribution to journalArticlepeer-review


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 letter, 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 (accompanying videos can be seen here: https://youtu.be/AQuuQ-h4dBM).

Original languageEnglish (US)
Article number9146673
Pages (from-to)6129-6136
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
StatePublished - Oct 2020


  • Reinforcement learning
  • compliance and impedance control
  • motion control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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