Autonomous Task Planning and Execution

Solving arbitrary manipulation tasks is a key feature for humanoid service robots. However, especially when tasks involve handling complex mechanisms or using tools, a generic action description is hard to define. Different objects require different handling methods. Therefore, a modular system architecture has been developed to autonomously solve manipulation tasks from the object point of view.

Object knowledge is used to parameterize the hybrid reasoning procedure.

Functional Object Classes:

  • Objects categorized by functionality in a hierarchical structure
  • Description of generic process models on the symbolic and geometric level via action templates

Hybrid Reasoning based on Object Knowledge:

  • An object storage provides prior object knowledge
  • The current world state is used as initial state for the symbolic planner
  • Action templates ground the symbolic planners outcome
  • Individual robot components are addressed on the geometric level
Serving ketchup out of a bottle.
Serving ketchup out of a dispenser.
Cutting a ribbon with a hedge shear.

Selected Publications

Daniel Leidner, Christoph Borst, and Gerd Hirzinger, "Things Are Made for What They Are: Solving Manipulation Tasks by Using Functional Object Classes", in Proc. of the IEEE-RAS International Conference on Humanoid Robots, Osaka, Japan, November 2012, pp. 429–435.

Contact

Daniel Leidner

Institute of Robotics and Mechatronics
Cognitive Robotics
Münchener Straße 20, 82234 Oberpfaffenhofen-Weßling