移动中抓取任意物体的自监督学习
Self-Supervised Learning of Grasping Arbitrary Objects On-the-Move
AI总结：
本研究提出一种基于自监督学习的移动抓取方法，通过动作原语化和两阶段学习训练FCN模型预测抓取与移动参数，实现了高准确率和强泛化能力的移动抓取。
摘要：
移动抓取通过利用机器人的移动性来提高操作效率。本研究旨在使商用现成机器人能够进行移动抓取，这需要精确的定时和姿态调整。自监督学习可以开发一种可泛化的策略，以调整机器人的速度，并根据目标物体的形状和姿态确定抓取位置和方向。由于移动抓取的复杂性，动作原语化和逐步学习对于避免在试错学习中出现数据稀疏至关重要。本研究将移动抓取简化为两个抓取动作原语和一个移动动作原语，这些原语可以在机械臂的有限自由度下操作。本研究引入了三个全卷积神经网络（FCN）模型，用于从视觉输入中预测静态抓取原语、动态抓取原语和残余移动速度误差。两阶段抓取学习方法促进了FCN模型的无缝学习。消融研究表明，所提出的方法实现了最高的抓取准确率和拾取与放置效率。此外，在模拟中随机化物体形状和环境，有效地实现了可泛化的移动抓取。
英文摘要：
Mobile grasping enhances manipulation efficiency by utilizing robots' mobility. This study aims to enable a commercial off-the-shelf robot for mobile grasping, requiring precise timing and pose adjustments. Self-supervised learning can develop a generalizable policy to adjust the robot's velocity and determine grasp position and orientation based on the target object's shape and pose. Due to mobile grasping's complexity, action primitivization and step-by-step learning are crucial to avoid data sparsity in learning from trial and error. This study simplifies mobile grasping into two grasp action primitives and a moving action primitive, which can be operated with limited degrees of freedom for the manipulator. This study introduces three fully convolutional neural network (FCN) models to predict static grasp primitive, dynamic grasp primitive, and residual moving velocity error from visual inputs. A two-stage grasp learning approach facilitates seamless FCN model learning. The ablation study demonstrated that the proposed method achieved the highest grasping accuracy and pick-and-place efficiency. Furthermore, randomizing object shapes and environments in the simulation effectively achieved generalizable mobile grasping.
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。
