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Conference Paper

A Flexible Hybrid Framework for Modeling Complex Manipulation Tasks

MPS-Authors
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Kroemer,  Oliver
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Peters,  J.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Kroemer, O., & Peters, J. (2011). A Flexible Hybrid Framework for Modeling Complex Manipulation Tasks. In IEEE International Conference on Robotics and Automation (ICRA 2011) (pp. 1856-1861).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-4CFF-B
Abstract
Future service robots will need to perform a wide range of tasks using various objects. In order to perform complex tasks, robots require a suitable internal representation of the task. We propose a hybrid framework for representing manipulation tasks, which combines continuous motion planning and discrete task-level planning. In addition, we use a mid-level planner to optimize individual actions according to the plan. The proposed framework incorporates biologically-inspired concepts, such as affordances and motor primitives, in order to efficiently plan for manipulation tasks. The final framework is modular, can generalize well to different situations, and is straightforward to expand. Our demonstrations also show how the use of affordances and mid-level planning can lead to improved performance.