# Publications Freek Stulp

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 • Sorted by Date • Classified by Publication Type • Classified by Research Category • Action-Related Place-Based Mobile Manipulation Freek Stulp, Andreas Fedrizzi, and Michael Beetz. Action-Related Place-Based Mobile Manipulation. In International Conference on Intelligent Robots and Systems (IROS), 2009. Download [PDF]1.9MB Abstract In mobile manipulation, the position to which the robot navigates has a large influence on the ease with which a subsequent manipulation action can be performed. Whether a manipulation action succeeds depends on many factors, such as the robot's hardware configuration, the controllers the robot uses to achieve navigation and manipulation, the task context, and uncertainties in state estimation. In this paper, we present \arpplace', an action-related place which takes these factors, and the context in which the actions are performed into account. Through experience-based learning, the robot first learns a so-called generalized success model, which discerns between positions from which manipulation succeeds or fails. On-line, this model is used to compute a \arpplace, a probability distribution that maps positions to a predicted probability of successful manipulation, and takes the uncertainty in the robot and object's position into account. In an empirical evaluation, we demonstrate that using \arpplaces for least-commitment navigation improves the success rate of subsequent manipulation tasks substantially. BibTeX
 @InProceedings{stulp09actionrelated, title = {Action-Related Place-Based Mobile Manipulation}, author = {Freek Stulp and Andreas Fedrizzi and Michael Beetz}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, year = {2009}, abstract = {In mobile manipulation, the position to which the robot navigates has a large influence on the ease with which a subsequent manipulation action can be performed. Whether a manipulation action succeeds depends on many factors, such as the robot's hardware configuration, the controllers the robot uses to achieve navigation and manipulation, the task context, and uncertainties in state estimation. In this paper, we present \arpplace', an action-related place which takes these factors, and the context in which the actions are performed into account. Through experience-based learning, the robot first learns a so-called \emph{generalized success model}, which discerns between positions from which manipulation succeeds or fails. On-line, this model is used to compute a \arpplace, a probability distribution that maps positions to a predicted probability of successful manipulation, and takes the uncertainty in the robot and object's position into account. In an empirical evaluation, we demonstrate that using \arpplace{}s for least-commitment navigation improves the success rate of subsequent manipulation tasks substantially. }, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Action-Related Places for Mobile Manipulation} } 

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