Grasping is the process of picking an object by applying forces and torques at a set of contacts. Recent advances in deep-learning methods have allowed rapid progress in robotic object grasping. We systematically surveyed the publications over the last decade, with a particular interest in grasping an object using all 6 degrees of freedom of the end-effector pose. Our review found four common methodologies for robotic grasping: sampling-based approaches, direct regression, reinforcement learning, and exemplar approaches. Furthermore, we found two 'supporting methods' around grasping that use deep-learning to support the grasping process, shape approximation, and affordances. We have distilled the publications found in this systematic review (85 papers) into ten key takeaways we consider crucial for future robotic grasping and manipulation research. An online version of the survey is available at https://rhys-newbury.github.io/projects/6dof/
翻译:在一系列接触中,通过运用力量和托盘来挑选物体的过程。最近深层学习方法的进展使得机器人物体的捕捉工作能够迅速取得进展。我们系统调查了过去十年中的出版物,特别有兴趣利用最终效应构成的所有6度自由来捕捉物体。我们的审查发现了四种共同的机器人捕捉方法:抽样方法、直接回归、强化学习和示范方法。此外,我们发现两种“支持方法”围绕着利用深层学习来支持捕捉过程、形状近似和支付能力。我们已经将这次系统审查(85篇论文)中发现的出版物蒸馏成我们认为对未来机器人捕捉和操纵研究至关重要的10个关键取物。调查的在线版本可在https://rhys-newbury.github.io/project/6dof/https://rhys-newbury.github.