# Publications Freek Stulp

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 • Sorted by Date • Classified by Publication Type • Classified by Research Category • Generality and Legibility in Mobile Manipulation - Learning Skills for Routine Tasks Michael Beetz, Freek Stulp, Piotr Esden-Tempski, Andreas Fedrizzi, Ulrich Klank, Ingo Kresse, Alexis Maldonado, and Federico Ruiz. Generality and Legibility in Mobile Manipulation - Learning Skills for Routine Tasks. Autonomous Robots: Special Issue on Autonomous Mobile Manipulation, 28(1):21–44, January 2010. Download [PDF]14.7MB Abstract This article investigates methods for achieving more general manipulation capabilities for mobile manipulation platforms, which produce legible behavior in human living environments. To achieve generality and legibility, we combine two control mechanisms. First of all, experience- and observation-based learning of skills is applied to routine tasks, so that the repetitive and stereotypical character of everyday activity is exploited. Second, we use planning, reasoning, and search for novel tasks which have no stereotypical solution. We apply these ideas to the learning and use of action-related places, to the model-based visual recognition and localization of objects, and the learning and application of reaching strategies and motions from humans. We demonstrate the integration of these mechanisms into a single low-level control system for autonomous manipulation platforms. BibTeX
 @Article{beetz10generality, title = {Generality and Legibility in Mobile Manipulation - Learning Skills for Routine Tasks}, author = {Michael Beetz and Freek Stulp and Piotr Esden-Tempski and Andreas Fedrizzi and Ulrich Klank and Ingo Kresse and Alexis Maldonado and Federico Ruiz}, journal = {Autonomous Robots: Special Issue on Autonomous Mobile Manipulation}, year = {2010}, month = {January}, number = {1}, pages = {21-44}, volume = {28}, abstract = { This article investigates methods for achieving more general manipulation capabilities for mobile manipulation platforms, which produce legible behavior in human living environments. To achieve generality and legibility, we combine two control mechanisms. First of all, experience- and observation-based learning of skills is applied to \emph{routine tasks}, so that the repetitive and stereotypical character of everyday activity is exploited. Second, we use planning, reasoning, and search for \emph{novel tasks} which have no stereotypical solution. We apply these ideas to the learning and use of action-related places, to the model-based visual recognition and localization of objects, and the learning and application of reaching strategies and motions from humans. We demonstrate the integration of these mechanisms into a single low-level control system for autonomous manipulation platforms. }, bib2html_pubtype = {Journal}, bib2html_rescat = {Action-Related Places for Mobile Manipulation} } 

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