Imitation Learning With Additional Constraints on Motion Style Using Parametric Bias
Imitation Learning With Additional Constraints on Motion Style Using Parametric Bias
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DOI：
10.1109/lra.2021.3087423
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发表时间：
2021-07-01
影响因子：
5.2
通讯作者：
Inaba, Masayuki
中科院分区：
文献类型：
--
作者：
Kawaharazuka, Kento;Kawamura, Yoichiro;Inaba, Masayuki
模仿学习是在机器人中自适应地再现人类演示的方法之一。到目前为止，人们发现模仿学习的泛化能力使机器人能够在未经训练的环境中适应性地执行任务。然而，诸如运动轨迹和施加的力的大小等运动风格很大程度上取决于人类演示的数据集，并最终确定为平均运动风格。在本研究中，我们提出了一种在传统模仿学习网络中添加参数偏差的方法，并且可以对运动风格添加约束。通过使用 PR2 和肌肉骨骼人形 MusashiLarm 进行的实验，我们表明可以通过在关节速度、肌肉长度速度和肌肉张力的约束下按预期改变其运动方式来执行任务。
Imitation learning is one of the methods for reproducing human demonstration adaptively in robots. So far, it has been found that generalization ability of the imitation learning enables the robots to perform tasks adaptably in untrained environments. However, motion styles such as motion trajectory and the amount of force applied depend largely on the dataset of human demonstration, and settle down to an average motion style. In this study, we propose a method that adds parametric bias to the conventional imitation learning network and can add constraints to the motion style. By experiments using PR2 and the musculoskeletal humanoid MusashiLarm, we show that it is possible to perform tasks by changing its motion style as intended with constraints on joint velocity, muscle length velocity, and muscle tension.