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.

翻译：利用机器学习研究抓取可供性在过去数十年间受到广泛关注。现有文献为后续研究奠定了坚实基础，但仍存在若干可改进之处。现有成果通常聚焦于抓取构型，很少从可达性与轨迹规划角度考虑抓取动作的（再）生成方式。此外，当前多数学习方法仅关注生成单一可行抓取，既未揭示结果生成过程，也缺乏对抓取鲁棒性的深入解析。本文提出一种新的抓取可供性学习视角，显式考虑抓取合成过程——即如何利用机械臂运动学实现抓取动作的具体化。该方法能够显式映射抓取策略空间，涵盖生成的抓取类型及其对应抓取质量。数值仿真结果验证了该方法的有效性，并揭示其如何为原本不透明的强化学习过程提供更高程度的可解释性。