Nowadays, a number of grasping algorithms have been proposed, that can predict a candidate of grasp poses, even for unseen objects. This enables a robotic manipulator to pick-and-place such objects. However, some of the predicted grasp poses to stably lift a target object may not be directly approachable due to workspace limitations. In such cases, the robot will need to re-grasp the desired object to enable successful grasping on it. This involves planning a sequence of continuous actions such as sliding, re-grasping, and transferring. To address this multi-modal problem, we propose a Markov-Decision Process-based multi-modal planner that can rearrange the object into a position suitable for stable manipulation. We demonstrate improved performance in both simulation and the real world for pick-and-place tasks.

翻译：近年来，研究者提出了大量抓取算法，这些算法能够预测抓取姿态的候选方案，即使针对未知物体也不例外。这使得机械臂能够实现对此类物体的拾取与放置操作。然而，部分用于稳定举起目标物体的预测抓取姿态，可能因工作空间限制而无法直接接近。在这种情况下，机器人需要对目标物体进行再抓取操作，以实现成功抓取。这需要规划一系列连续动作，包括滑动、再抓取与传递。为解决这一多模态问题，我们提出一种基于马尔可夫决策过程的多模态规划器，该规划器能够将物体重新排列至适合稳定操作的位置。我们在仿真环境与真实世界中均验证了该方法在拾取与放置任务中的性能提升。