Mobile manipulation constitutes a fundamental task for robotic assistants and garners significant attention within the robotics community. A critical challenge inherent in mobile manipulation is the effective observation of the target while approaching it for grasping. In this work, we propose a graspability-aware mobile manipulation approach powered by an online grasping pose fusion framework that enables a temporally consistent grasping observation. Specifically, the predicted grasping poses are online organized to eliminate the redundant, outlier grasping poses, which can be encoded as a grasping pose observation state for reinforcement learning. Moreover, on-the-fly fusing the grasping poses enables a direct assessment of graspability, encompassing both the quantity and quality of grasping poses.

翻译：移动操作构成了机器人助手的基础任务，并在机器人学界引起了广泛关注。移动操作中一个关键挑战是在接近目标进行抓取时如何有效观测目标。本研究提出了一种基于可抓取性感知的移动操作方法，该方法由在线抓取位姿融合框架驱动，可实现时间一致的抓取观测。具体而言，预测的抓取位姿被在线组织以消除冗餘和异常的抓取位姿，并能编码为强化学习的抓取位姿观测状态。此外，实时融合抓取位姿使得能够直接评估可抓取性，包括抓取位姿的数量和质量两个维度。