PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A flexible hybrid framework for modeling complex manipulation tasks
Oliver Kroemer and Jan Peters
In: nternational Conference on Robotics and Automation, 9-13 May 2011, China.

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8034
Deposited By:Oliver Kroemer
Deposited On:17 March 2011