Transformational planning for mobile manipulation based on action-related places
Andreas Fedrizzi, Lorenz Mösenlechner, Freek Stulp, Michael Beetz · 2009
Abstract — Opportunities for interleaving or parallelizing actions are abundant in everyday activities. Being able to perceive, predict and exploit such opportunities leads to more efficient and robust behavior. In this paper, we present a mobile manipulation platform that exploits such opportunities to optimize its behavior, e.g. grasping two objects from one location simultaneously, rather than navigating to two different locations. To do so, it uses a general least-commitment repre-sentation of place, called ARPLACE, from which manipulation is predicted to be successful. Models for ARPLACEs are learned from experience using Support Vector Machines and Point Dis-tribution Models, and take into account the robot’s morphology and skill repertoire. We present a transformational planner that reasons about ARPLACEs, and applies transformation rules to its plans if more robust and efficient behavior is predicted. I.