Grasping occluded objects in cluttered environments is an essential component in complex robotic manipulation tasks. In this paper, we introduce an AffordanCE-driven Next-Best-View planning policy (ACE-NBV) that tries to find a feasible grasp for target object via continuously observing scenes from new viewpoints. This policy is motivated by the observation that the grasp affordances of an occluded object can be better-measured under the view when the view-direction are the same as the grasp view. Specifically, our method leverages the paradigm of novel view imagery to predict the grasps affordances under previously unobserved view, and select next observation view based on the highest imagined grasp quality of the target object. The experimental results in simulation and on a real robot demonstrate the effectiveness of the proposed affordance-driven next-best-view planning policy. Project page: https://sszxc.net/ace-nbv/.

翻译：在杂乱环境中抓取被遮挡物体是复杂机器人操作任务的关键组成部分。本文提出一种基于功能驱动的下一最佳视角规划策略（ACE-NBV），该策略通过持续从新视角观察场景，为目标物体寻找可行抓取方案。该策略的动机源于以下观察：当观测方向与抓取视角一致时，被遮挡物体的抓取功能性可得到更优度量。具体而言，本方法利用新颖视角图像生成范式，预测未观测视角下的抓取功能性，并基于目标物体的最高想象抓取质量选择下一个观测视角。仿真实验与真实机器人实验均证明了所提出的功能驱动型下一最佳视角规划策略的有效性。项目页面：https://sszxc.net/ace-nbv/。