Residual learning from demonstration: adapting DMPs for contact-rich manipulation

Todor Bozhinov Davchev, Kevin Sebastian Luck, Michael Burke, Franziska Meier, Stefan Schaal, Subramanian Ramamoorthy

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Manipulation skills involving contact and friction are inherent to many robotics tasks. Using the class of motor primitives for peg-in-hole like insertions, we study how robots can learn such skills. Dynamic Movement Primitives (DMP) are a popular way of extracting such policies through behaviour cloning (BC), but can struggle in the context of insertion. Policy adaptation strategies such as residual learning can help improve the overall performance of policies in the context of contact-rich manipulation. However, it is not clear how to best do this with DMPs. As result, we consider a number of possible ways for adapting a DMP formulation and propose ``residual Learning from Demonstration`` (rLfD), a framework that combines DMPs with Reinforcement Learning (RL) to learn a residual correction policy. Our evaluations suggest that applying residual learning directly in task space and operating on the full pose of the robot can significantly improve the overall performance of DMPs. We show that rLfD offers a gentle to the joints solution that improves the task success and generalisation of DMPs and enables transfer to different geometries and frictions through few-shot task adaptation. The proposed framework is evaluated on a set of tasks in which a simulated robot and a real physical robot arm have to successfully insert pegs, gears and plugs into their respective sockets. Further material and videos accompanying this paper are provided at https://sites.google.com/view/rlfd/.

Original languageEnglish
Pages (from-to)4488-4495
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number2
DOIs
Publication statusPublished - Apr 2022

Keywords

  • Adaptation models
  • Couplings
  • Friction
  • Gears
  • Learning from Demonstration
  • Reinforcement Learning
  • Robots
  • Sensorimotor Learning
  • Task analysis
  • Trajectory

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