Adaptation of manipulation skills in physical contact with the environment to reference force profiles

Fares J. Abu-Dakka, Bojan Nemec, Jimmy A. Jørgensen, Thiusius R. Savarimuthu, Norbert Krüger, Aleš Ude

Research output: Contribution to journalArticlepeer-review


We propose a new methodology for learning and adaption of manipulation skills that involve physical contact with the environment. Pure position control is unsuitable for such tasks because even small errors in the desired trajectory can cause significant deviations from the desired forces and torques. The proposed algorithm takes a reference Cartesian trajectory and force/torque profile as input and adapts the movement so that the resulting forces and torques match the reference profiles. The learning algorithm is based on dynamic movement primitives and quaternion representation of orientation, which provide a mathematical machinery for efficient and stable adaptation. Experimentally we show that the robot’s performance can be significantly improved within a few iteration steps, compensating for vision and other errors that might arise during the execution of the task. We also show that our methodology is suitable both for robots with admittance and for robots with impedance control.

Original languageEnglish (US)
Pages (from-to)199-217
Number of pages19
JournalAutonomous Robots
Issue number2
StatePublished - Aug 23 2015


  • Manipulation and compliant assembly
  • Physical human-robot interaction
  • Programming by demonstration
  • Skill learning and adaptation

ASJC Scopus subject areas

  • Artificial Intelligence


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