Stüber Jochen, Zito Claudio, Stolkin Rustam
IRLab, School of Computer Science, University of Birmingham, Birmingham, United Kingdom.
Extreme Robotics Lab (ERL), University of Birmingham, Birmingham, United Kingdom.
Front Robot AI. 2020 Feb 6;7:8. doi: 10.3389/frobt.2020.00008. eCollection 2020.
As robots make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable circumstances. With this regard, pushing is an essential motion primitive that dramatically extends a robot's manipulation repertoire. In this work, we review the robotic pushing literature. While focusing on work concerned with predicting the motion of pushed objects, we also cover relevant applications of pushing for planning and control. Beginning with analytical approaches, under which we also subsume physics engines, we then proceed to discuss work on learning models from data. In doing so, we dedicate a separate section to deep learning approaches which have seen a recent upsurge in the literature. Concluding remarks and further research perspectives are given at the end of the paper.
随着机器人从工厂走进人类环境、外太空及其他领域,它们需要具备在各种不可预见的情况下操纵环境的技能。在这方面,推是一种基本的运动原语,能极大地扩展机器人的操作技能。在这项工作中,我们回顾了机器人推操作的文献。在关注与预测被推物体运动相关工作的同时,我们也涵盖了推操作在规划和控制方面的相关应用。从分析方法(我们也将物理引擎归入其中)开始,然后我们继续讨论从数据中学习模型的工作。在此过程中,我们专门用一个章节来讨论深度学习方法,该方法在近期的文献中大量涌现。论文结尾给出了总结性评论和进一步的研究展望。