论文标题
COGRASP：人类机器人合作的6多dof Grasp Generation
CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration
论文作者
论文摘要
机器人握把是一个积极研究的机器人技术领域，主要关注用于物体操纵的生成的抓取质量。但是，尽管有进步，这些方法并未考虑人类机器人协作设置，在这些设置中，机器人和人类将不得不同时掌握相同的对象。因此，为确保安全自然的协作体验所必需，生成与人类同时持有对象的人类偏好兼容的机器人grasps。在本文中，我们提出了一种新型的，基于神经网络的方法，称为Cograsp，该方法通过将对象的人类偏好模型抓住在机器人掌握的选择过程中，从而产生人类意识的机器人掌握。我们通过模拟和实体机器人实验和用户研究来验证我们的方法与现有的最新机器人抓地方法。在实际的机器人实验中，我们的方法在产生稳定的抓地力方面达到了约88％的成功率，这也允许人类以社会符合性的方式同时相互作用和掌握对象。此外，我们与10位独立参与者的用户研究表明，与标准的机器人抓握技术相比，我们的方法可以使安全，自然和具有社会意识的人类机器人的共磨垫体验。
Robot grasping is an actively studied area in robotics, mainly focusing on the quality of generated grasps for object manipulation. However, despite advancements, these methods do not consider the human-robot collaboration settings where robots and humans will have to grasp the same objects concurrently. Therefore, generating robot grasps compatible with human preferences of simultaneously holding an object becomes necessary to ensure a safe and natural collaboration experience. In this paper, we propose a novel, deep neural network-based method called CoGrasp that generates human-aware robot grasps by contextualizing human preference models of object grasping into the robot grasp selection process. We validate our approach against existing state-of-the-art robot grasping methods through simulated and real-robot experiments and user studies. In real robot experiments, our method achieves about 88\% success rate in producing stable grasps that also allow humans to interact and grasp objects simultaneously in a socially compliant manner. Furthermore, our user study with 10 independent participants indicated our approach enables a safe, natural, and socially-aware human-robot objects' co-grasping experience compared to a standard robot grasping technique.
