1. Transformational Planning for Mobile Manipulation based on Action-related Places Andreas Fedrizzi, Lorenz Mösenlechner, Freek Stulp, Michael Beetz Intelligent Autonomous Systems, Technische Universität München
In mobile manipulation, positions from where objects can be grasped depend very much on the robot's skills and morphology
We propose ARPlaces, which map robot positions to grasp success probabilities, and are learned from observed experience
ARPlaces are used to find high quality positions for performing subsequent manipulation tasks
As our control programs are written in RPL, ARPlaces can be merged easily by a transformational planner
This leads to more robust and efficient plans (average exec. time: 48s -> 32s)