Pick and Place Planning is Better Than Pick Planning Then Place Planning
Pick and Place Planning is Better Than Pick Planning Then Place Planning
复制标题
DOI：
10.1109/lra.2024.3360892
复制
发表时间：
2024-01
影响因子：
5.2
通讯作者：
M. Shanthi;Tucker Hermans
中科院分区：
文献类型：
--
作者：
M. Shanthi;Tucker Hermans
机器人拾取和放置是自主操作的核心。当在杂乱或复杂的环境中进行时，机器人必须联合推理所选择的抓取和期望的放置位置，以确保成功。虽然有几个作品已经研究了这个联合拾取和放置问题，没有充分利用最近的学习为基础的多指抓取规划的方法。我们提出了一个模块化的联合拾取和放置规划算法，可以利用国家的最先进的掌握分类器规划多指掌握新的对象从部分视点云。我们展示了我们的联合挑选和放置配方与不同的放置任务相关的几个成本。使用物理机器人在混乱场景中进行拾取和放置任务的实验表明，我们的联合推理方法比顺序拾取然后放置方法更成功，同时也实现了更好的放置配置。
Robotic pick and place stands at the heart of autonomous manipulation. When conducted in cluttered or complex environments robots must jointly reason about the selected grasp and desired placement locations to ensure success. While several works have examined this joint pick-and-place problem, none have fully leveraged recent learning-based approaches for multi-fingered grasp planning. We present a modular algorithm for joint pick and place planning that can make use of state of the art grasp classifiers for planning multi-fingered grasps for novel objects from partial view point clouds. We demonstrate our joint pick and place formulation with several costs associated with different placement tasks. Experiments on pick and place tasks with cluttered scenes using a physical robot show that our joint inference method is more successful than a sequential pick then place approach, while also achieving better placement configurations.