  To perform versatile mobile manipulation tasks in human-centered
environments, the ability to efficiently transfer learned tasks and experiences
from one robot to another or across different environments is key. In this
paper, we present MAkEable, a versatile uni- and multi-manual mobile
manipulation framework that facilitates the transfer of capabilities and
knowledge across different tasks, environments, and robots. Our framework
integrates an affordance-based task description into the memory-centric
cognitive architecture of the ARMAR humanoid robot family, which supports the
sharing of experiences and demonstrations for transfer learning. By
representing mobile manipulation actions through affordances, i.e., interaction
possibilities of the robot with its environment, we provide a unifying
framework for the autonomous uni- and multi-manual manipulation of known and
unknown objects in various environments. We demonstrate the applicability of
the framework in real-world experiments for multiple robots, tasks, and
environments. This includes grasping known and unknown objects, object placing,
bimanual object grasping, memory-enabled skill transfer in a drawer opening
scenario across two different humanoid robots, and a pouring task learned from
human demonstration.
