利用以双边控制为基础的模拟学习和自动递减学习来创造动议 (Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning)

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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.

翻译：可以代表人类自动执行各种任务的机器人正在成为机器人领域日益重要的研究重点。模拟学习作为一种高效和高性能方法进行了研究,并提议将双边控制基础上的模拟学习作为一种能够实现快速运动的方法。然而,由于这种方法无法实施自动递减学习,这种方法可能不会产生可取的长期行为。因此,我们在本文件中提出了一种用于双边控制模拟学习的自动递减学习方法。提出了用于实施自动递减学习的新神经网络模型。本研究中,进行了三种类型的实验,以核实拟议方法的有效性。与常规方法相比,业绩得到改进;拟议方法的成功率最高。由于拟议模型的结构和自动递减学习,拟议方法可以产生成功任务所需的动力,并具有环境变化的高度普遍化能力。