@inproceedings{Gaschler2013c, author = {Gaschler, Andre and Petrick, Ronald P. A. and Giuliani, Manuel and Rickert, Markus and Knoll, Alois}, title = {{KVP}: A Knowledge of Volumes Approach to Robot Task Planning}, booktitle = {Proceedings of the {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}, pages = {202--208}, year = {2013}, month = nov, address = {Tokyo, Japan}, abstract = {Robot task planning is an inherently challenging problem, as it covers both continuous-space geometric reasoning about robot motion and perception, as well as purely symbolic knowledge about actions and objects. This paper presents a novel "knowledge of volumes" framework for solving generic robot tasks in partially known environments. In particular, this approach (abbreviated, KVP) combines the power of symbolic, knowledge-level AI planning with the efficient computation of volumes, which serve as an intermediate representation for both robot action and perception. While we demonstrate the effectiveness of our framework in a bimanual robot bartender scenario, our approach is also more generally applicable to tasks in automation and mobile manipulation, involving arbitrary numbers of manipulators.}, doi = {10.1109/IROS.2013.6696354}, keywords = {robotics, james}, }