Behavioral Manifolds: Representing the Landscape of Grasp Affordances in Relative Pose Space

Abstract

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. 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 application to a range of objects illustrate merit of the method and highlight the manner in which it may promote a greater degree of explainability for otherwise intransparent reinforcement processes.