  Moving a human body or a large and bulky object can require the strength of
whole arm manipulation (WAM). This type of manipulation places the load on the
robot's arms and relies on global properties of the interaction to
succeed---rather than local contacts such as grasping or non-prehensile
pushing. In this paper, we learn to generate motions that enable WAM for
holding and transporting of humans in certain rescue or patient care scenarios.
We model the task as a reinforcement learning problem in order to provide a
behavior that can directly respond to external perturbation and human motion.
For this, we represent global properties of the robot-human interaction with
topology-based coordinates that are computed from arm and torso positions.
These coordinates also allow transferring the learned policy to other body
shapes and sizes. For training and evaluation, we simulate a dynamic sea rescue
scenario and show in quantitative experiments that the policy can solve unseen
scenarios with differently-shaped humans, floating humans, or with perception
noise. Our qualitative experiments show the subsequent transporting after
holding is achieved and we demonstrate that the policy can be directly
transferred to a real world setting.
