论文标题
生成特定任务的机器人掌握
Generating Task-specific Robotic Grasps
论文作者
论文摘要
本文介绍了一种通过共同考虑稳定性以及其他任务和对象特定约束来生成机器人抓取的方法。我们介绍了一个三级表示，该表示将从少数对象，任务和相关范围的示例中为每个对象类获取。表示表示每个对象类的特定于任务知识是一个关键点骨架之间的关系和合适的抓地点之间的关系，尽管规模和方向上有阶级内部变化，但保留了这些知识。通过一种简单的基于采样的方法来调查学习模型，以指导生成grasps，以平衡任务和稳定性约束。我们在Franka Emika Panda机器人的背景下进行基础并评估我们的方法，该机器人协助人类采摘机器人没有先前CAD模型的桌面对象。实验结果表明，与仅关注稳定性的基线方法相比，我们的方法能够为不同任务提供合适的掌握。
This paper describes a method for generating robot grasps by jointly considering stability and other task and object-specific constraints. We introduce a three-level representation that is acquired for each object class from a small number of exemplars of objects, tasks, and relevant grasps. The representation encodes task-specific knowledge for each object class as a relationship between a keypoint skeleton and suitable grasp points that is preserved despite intra-class variations in scale and orientation. The learned models are queried at run time by a simple sampling-based method to guide the generation of grasps that balance task and stability constraints. We ground and evaluate our method in the context of a Franka Emika Panda robot assisting a human in picking tabletop objects for which the robot does not have prior CAD models. Experimental results demonstrate that in comparison with a baseline method that only focuses on stability, our method is able to provide suitable grasps for different tasks.
