Autonomous Robots

, Volume 39, Issue 2, pp 199–217 | Cite as

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


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.


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



The research leading to these results has received funding from the European Community Seventh Framework Programme FP7/2007-2013 (Specific Programme Cooperation, Theme 3, Information and Communication Technologies) under Grant Agreement No. 269959, IntellAct and No. 600578, ACAT.

Supplementary material

Supplementary material 1 (mp4 2450 KB)

Supplementary material 2 (mp4 6842 KB)


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Fares J. Abu-Dakka
    • 1
  • Bojan Nemec
    • 1
  • Jimmy A. Jørgensen
    • 2
  • Thiusius R. Savarimuthu
    • 2
  • Norbert Krüger
    • 2
  • Aleš Ude
    • 1
  1. 1.Humanoid and Cognitive Robotics Lab, Department of Automatics, Biocybernetics and RoboticsJožef Stefan InstituteLjubljanaSlovenia
  2. 2.Cognitive Vision Lab, Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkOdenseDenmark

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