  The use of machine learning to investigate grasp affordances has received
extensive attention over the past several decades. The existing literature
provides a robust basis to build upon, though a number of aspects may be
improved. Results commonly work in terms of grasp configuration, with little
consideration for the manner in which the grasp may be (re-)produced from a
reachability and trajectory planning perspective. In addition, the majority of
existing learning approaches focus of producing a single viable grasp, offering
little transparency on how the result was reached, or insights on its
robustness. We propose a different perspective on grasp affordance learning,
explicitly accounting for grasp synthesis; that is, the manner in which
manipulator kinematics are used to allow materialization of grasps. The
approach allows to explicitly map the grasp policy space in terms of generated
grasp types and associated grasp quality. Results of numerical simulations
illustrate merit of the method and highlight the manner in which it may promote
a greater degree of explainability for otherwise intransparent reinforcement
processes.
