用于机器人抓取的可供性驱动下一最佳视角规划
Affordance-Driven Next-Best-View Planning for Robotic Grasping
AI总结：
针对杂乱场景中遮挡物体难以抓取的问题，本文提出ACE-NBV，通过新视角想象预测抓取可供性并选择下一最佳视角，仿真与真实机器人实验验证了其有效性。
摘要：
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重要组成部分。本文提出一种可供性驱动的下一最佳视角规划策略（AffordanCE-driven Next-Best-View planning policy，ACE-NBV），通过持续从新视角观察场景，尝试为目标物体寻找可行抓取。该策略的动机是观察到：当观察方向与抓取视角一致时，被遮挡物体的抓取可供性能够在该视角下得到更好测量。具体而言，本方法利用新视角图像生成范式，预测此前未观察视角下的抓取可供性，并依据目标物体最高的想象抓取质量选择下一观察视角。仿真和真实机器人上的实验结果证明了所提出的可供性驱动下一最佳视角规划策略的有效性。项目页面：https://sszxc.net/ace-nbv/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
