Systematically Exploring the Landscape of Grasp Affordances via Behavioral Manifolds

TL;DR

This paper introduces a novel approach to grasp affordance learning that emphasizes grasp synthesis and manipulator kinematics, enhancing explainability and robustness over traditional methods focused solely on grasp configuration.

Contribution

It presents a new perspective on grasp affordance learning by explicitly modeling grasp synthesis and the manipulation process, improving transparency and robustness.

Findings

Numerical simulations demonstrate the effectiveness of the proposed method.

The approach enhances explainability of grasp policies.

It maps grasp policy space in terms of grasp types and quality.

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. 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.