论文标题
使用基于双边控制的模仿学习和自回归学习的运动产生
Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning
论文作者
论文摘要
可以自动代表人类执行各种任务的机器人正在成为机器人技术领域研究的越来越重要的重点。模仿学习已被研究为一种有效且高性能的方法，并提出了基于双边控制的模仿学习，作为一种可以实现快速运动的方法。但是，由于该方法无法实施自回旋学习，因此该方法可能不会产生理想的长期行为。因此，在本文中，我们提出了一种基于双边控制的模仿学习的自回旋学习方法。提出了一种用于实施自回归学习的新神经网络模型。在这项研究中，进行了三种类型的实验，以验证该方法的有效性。与常规方法相比，性能得到了改善。所提出的方法的成功率最高。由于对所提出的模型的结构和自回旋学习，该方法可以为成功任务生成理想的运动，并具有很高的环境变化概括能力。
Robots that can execute various tasks automatically on behalf of humans are becoming an increasingly important focus of research in the field of robotics. Imitation learning has been studied as an efficient and high-performance method, and imitation learning based on bilateral control has been proposed as a method that can realize fast motion. However, because this method cannot implement autoregressive learning, this method may not generate desirable long-term behavior. Therefore, in this paper, we propose a method of autoregressive learning for bilateral control-based imitation learning. A new neural network model for implementing autoregressive learning is proposed. In this study, three types of experiments are conducted to verify the effectiveness of the proposed method. The performance is improved compared to conventional approaches; the proposed method has the highest rate of success. Owing to the structure and autoregressive learning of the proposed model, the proposed method can generate the desirable motion for successful tasks and have a high generalization ability for environmental changes.
