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

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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.

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Notes

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    commonly summarized as geometric parameters in the automated planning community.

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Acknowledgments

This work was partially funded by the European Community’s Seventh Framework Programme under Grant Agreement No. 608849 EuRoC and partially by the Helmholtz Association Project HVF-0029 RACE-LAB.

Author information

Correspondence to Daniel Leidner.

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Leidner, D., Dietrich, A., Beetz, M. et al. Knowledge-enabled parameterization of whole-body control strategies for compliant service robots. Auton Robot 40, 519–536 (2016). https://doi.org/10.1007/s10514-015-9523-3

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Keywords

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