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 gain of the highest imagined grasp quality of the target object. The experimental results in simulation and on the real robot demonstrate the effectiveness of the proposed affordance-driven next-best-view planning policy. Additional results, code, and videos of real robot experiments can be found in the supplementary materials.

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中的关键组成部分。本文提出了一种名为“功能驱动型最佳下一视角规划策略”（ACE-NBV）的方法，旨在通过不断从新视角观测场景，为目标物体寻找可行的抓取方案。该策略基于以下观察：当观测方向与抓取方向一致时，被遮挡物体的抓取功能可以更准确地被测量。具体而言，我们的方法利用新视角图像生成范式，预测先前未观测视角下的抓取功能，并根据目标物体的最高想象抓取质量的增益，选择下一观测视角。仿真实验和真实机器人实验的结果验证了该功能驱动型最佳下一视角规划策略的有效性。更多结果、代码及真实机器人实验视频可参见补充材料。