论文标题
深度学习方法来掌握综合：评论
Deep Learning Approaches to Grasp Synthesis: A Review
论文作者
论文摘要
抓握是通过在一组触点上施加力和扭矩来拾取对象的过程。深度学习方法的最新进展允许在机器人对象抓地力方面快速进步。在这项系统的综述中，我们在过去十年中调查了出版物，特别兴趣使用所有6个最终效果姿势的自由度抓住对象。我们的综述发现了四种用于机器人抓钩的常见方法：基于抽样的方法，直接回归，强化学习和示例方法。此外，我们发现了围绕抓握的两种``辅助方法''，它们使用深度学习来支撑抓地力，形状近似和负担能力。我们已经将本系统评论（85篇论文）中发现的出版物提炼为十个关键要点，我们认为对未来的机器人抓握和操纵研究至关重要。该调查的在线版本可从https://rhys-newbury.github.io/projects/6dof/获得
Grasping is the process of picking up 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. In this systematic review, we 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. Additionally, 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/
