  Grasping is a complex process involving knowledge of the object, the
surroundings, and of oneself. While humans are able to integrate and process
all of the sensory information required for performing this task, equipping
machines with this capability is an extremely challenging endeavor. In this
paper, we investigate how deep learning techniques can allow us to translate
high-level concepts such as motor imagery to the problem of robotic grasp
synthesis. We explore a paradigm based on generative models for learning
integrated object-action representations, and demonstrate its capacity for
capturing and generating multimodal, multi-finger grasp configurations on a
simulated grasping dataset.
