论文标题
从人类演示中抓取任务
Task-grasping from human demonstration
论文作者
论文摘要
机器人掌握的一个挑战是要实现任务处理，那就是选择一个有利于掌握之前和之后任务成功的掌握。解决这一困难的框架之一是从观察中学习（LFO），它从人类示范中获得了各种暗示。本文在LFO框架中解决了三个问题：1）如何在功能上模仿具有有限掌握能力的机器人的人为示意的抓地力，2）如何将掌握技能与伸手模仿的身体进行协调，3）如何在物体下稳健地执行grasps pose pose and Shape new note不确定。提出了使用基于接触 - WEB的奖励和域随机化方向进行深入的加强学习，以实现这种强大的模仿抓握能力。实验结果表明，训练有素的抓握技能可以应用于LFO系统并在真实机器人上执行。另外，据表明，受过训练的技能对物体姿势的错误和对象形状的不确定性具有鲁棒性，并且可以与各种触及协调结合使用。
A challenge in robot grasping is to achieve task-grasping which is to select a grasp that is advantageous to the success of tasks before and after grasps. One of the frameworks to address this difficulty is Learning-from-Observation (LfO), which obtains various hints from human demonstrations. This paper solves three issues in the grasping skills in the LfO framework: 1) how to functionally mimic human-demonstrated grasps to robots with limited grasp capability, 2) how to coordinate grasp skills with reaching body mimicking, 3) how to robustly perform grasps under object pose and shape uncertainty. A deep reinforcement learning using contact-web based rewards and domain randomization of approach directions is proposed to achieve such robust mimicked grasping skills. Experiment results show that the trained grasping skills can be applied in an LfO system and executed on a real robot. In addition, it is shown that the trained skill is robust to errors in the object pose and to the uncertainty of the object shape and can be combined with various reach-coordination.
