Knowledge-enabled parameterization of whole-body control strategies for compliant service robots

Daniel Leidner, Alexander Dietrich, Michael Beetz, Alin Albu-Schäffer

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Compliant manipulation is one of the grand challenges for autonomous robots. Many household chores in human environments, such as cleaning the floor or wiping windows, rely on this principle. At the same time these tasks often require whole-body motions to cover a larger workspace. The performance of the actual task itself is thereby dependent on a large number of parameters that have to be taken into account. To tackle this issue we propose to utilize low-level compliant whole-body control strategies parameterized by high-level hybrid reasoning mechanisms. We categorize compliant wiping actions in order to determine relevant control parameters. According to these parameters we set up process models for each identified wiping action and implement generalized control strategies based on human task knowledge. We evaluate our approach experimentally on three whole-body manipulation tasks, namely scrubbing a mug with a sponge, skimming a window with a window wiper and bi-manually collecting the shards of a broken mug with a broom.

Original languageEnglish
Pages (from-to)519-536
Number of pages18
JournalAutonomous Robots
Volume40
Issue number3
DOIs
StatePublished - 1 Mar 2016

Keywords

  • AI reasoning methods
  • Humanoid robots
  • Mobile manipulation
  • Task knowledge
  • Whole-body control

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