Imitating human search strategies for assembly

Dennis Ehlers, Markku Suomalainen, Jens Lundell, Ville Kyrki

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)


We present a Learning from Demonstration method for teaching robots to perform search strategies imitated from humans in scenarios where alignment tasks fail due to position uncertainty. The method utilizes human demonstrations to learn both a state invariant dynamics model and an exploration distribution that captures the search area covered by the demonstrator. We present two alternative algorithms for computing a search trajectory from the exploration distribution, one based on sampling and another based on deterministic ergodic control. We augment the search trajectory with forces learnt through the dynamics model to enable searching both in force and position domains. An impedance controller with superposed forces is used for reproducing the learnt strategy. We experimentally evaluate the method on a KUKA LWR4+ performing a 2D peg-in-hole and a 3D electricity socket task. Results show that the proposed method can, with only few human demonstrations, learn to complete the search task.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Robotics and Automation, ICRA 2019
Number of pages7
ISBN (Electronic)9781538660263
Publication statusPublished - 1 May 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Robotics and Automation - Montreal, Canada
Duration: 20 May 201924 May 2019

Publication series

NameIEEE International Conference on Robotics and Automation
ISSN (Print)2152-4092
ISSN (Electronic)2379-9552


ConferenceIEEE International Conference on Robotics and Automation
Abbreviated titleICRA


  • Trajectory
  • Task analysis
  • Robot sensing systems
  • Search problems
  • Sockets
  • Impedance


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