Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration

Iñigo Iturrate*, Aljaz Kramberger, Christoffer Sloth

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

This paper presents a framework for programming in-contact tasks using learning by demonstration. The framework is demonstrated on an industrial gluing task, showing that a high quality robot behavior can be programmed using a single demonstration. A unified controller structure is proposed for the demonstration and execution of in-contact tasks that eases the transition from admittance controller for demonstration to parallel force/position control for the execution. The proposed controller is adapted according to the geometry of the task constraints, which is estimated online during the demonstration. In addition, the controller gains are adapted to the human behavior during demonstration to improve the quality of the demonstration. The considered gluing task requires the robot to alternate between free motion and in-contact motion; hence, an approach for minimizing contact forces during the switching between the two situations is presented. We evaluate our proposed system in a series of experiments, where we show that we are able to estimate the geometry of a curved surface, that our adaptive controller for demonstration allows users to achieve higher accuracy in a shorter demonstration duration when compared to an off-the-shelf controller for teaching implemented on a collaborative robot, and that our execution controller is able to reduce impact forces and apply a constant process force while adapting to the surface geometry.
Original languageEnglish
Article number767878
JournalFrontiers in Robotics and AI
Volume8
Number of pages18
ISSN2296-9144
DOIs
Publication statusPublished - 5. Nov 2021

Keywords

  • adaptive control
  • force control
  • gluing
  • learning from demonstration
  • parameter estimation

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  • PIRAT

    Sloth, C.

    01/10/201901/10/2023

    Project: Research

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