论文标题
通过行为塑造和新颖性搜索自动获取各种抓地力轨迹的曲目
Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Search
论文作者
论文摘要
抓住特定对象可能需要专用的握把运动，这也可能针对机器人最终效应器。在没有在机器人或对象上提出假设的情况下，确实没有任何通用和自主方法来生成这些运动。学习方法可以帮助自主发现相关的握把运动，但它们面临着一个重要的问题：抓握运动是如此罕见，以至于基于探索的学习方法几乎没有机会观察一个有趣的运动，从而引发了引导程序问题。我们介绍了一种产生各种抓握运动的方法，以解决这个问题。对于特定对象位置，在模拟中生成了动作。我们在几个模拟机器人上进行测试：百特，胡椒和库卡IIWA臂。尽管我们表明生成的动作实际上在真正的百特机器人上起作用，但目的是使用此方法来创建一个大型数据集来引导深度学习方法。
Grasping a particular object may require a dedicated grasping movement that may also be specific to the robot end-effector. No generic and autonomous method does exist to generate these movements without making hypotheses on the robot or on the object. Learning methods could help to autonomously discover relevant grasping movements, but they face an important issue: grasping movements are so rare that a learning method based on exploration has little chance to ever observe an interesting movement, thus creating a bootstrap issue. We introduce an approach to generate diverse grasping movements in order to solve this problem. The movements are generated in simulation, for particular object positions. We test it on several simulated robots: Baxter, Pepper and a Kuka Iiwa arm. Although we show that generated movements actually work on a real Baxter robot, the aim is to use this method to create a large dataset to bootstrap deep learning methods.
