论文标题
通过自我监督学习的学习速度，通过自我监督的学习进行非骚扰操作的模仿学习
Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed
论文作者
论文摘要
预计机器人将取代诸如家务之类的艰巨任务。其中一些任务包括执行的无毛线操作，而无需掌握对象。非忧虑操作非常困难，因为它需要考虑环境和对象的动态。因此，模仿复杂行为需要大量的人类示范。在这项研究中，提出了一种自我监督的学习，该学习认为动态以实现可变速度进行非骚扰操作。所提出的方法仅收集在自主操作期间获得的成功动作数据。通过微调成功的数据，机器人可以学习自身，环境和对象之间的动态。我们实验了使用对24种人体收集的培训数据训练的神经网络模型来挖出煎饼的任务。提出的方法将成功率从40.2％提高到85.7％，并成功完成了其他物体的任务超过75％。
Robots are expected to replace menial tasks such as housework. Some of these tasks include nonprehensile manipulation performed without grasping objects. Nonprehensile manipulation is very difficult because it requires considering the dynamics of environments and objects. Therefore imitating complex behaviors requires a large number of human demonstrations. In this study, a self-supervised learning that considers dynamics to achieve variable speed for nonprehensile manipulation is proposed. The proposed method collects and fine-tunes only successful action data obtained during autonomous operations. By fine-tuning the successful data, the robot learns the dynamics among itself, its environment, and objects. We experimented with the task of scooping and transporting pancakes using the neural network model trained on 24 human-collected training data. The proposed method significantly improved the success rate from 40.2% to 85.7%, and succeeded the task more than 75% for other objects.
