论文标题
基于双边控制的模仿学习应用的独立学习的分层模型
An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications
论文作者
论文摘要
最近，已经对机器学习进行动作进行了积极研究，以使各种任务自动化。模仿学习就是一种从事先收集的数据中学习动作的方法。但是，执行长期任务仍然具有挑战性。因此，提出了一个新颖的模仿学习框架来解决这个问题。所提出的框架包括上层和下层，其中的上层模型（其时间尺度很长，下层模型（其时间尺寸）很短，可以独立训练。在此模型中，上层学习长期任务计划，下层学习运动原始。在实验上，该方法与基于层次RNN的方法进行了比较，以验证其有效性。因此，提出的方法显示出等于或大于常规方法的成功率。此外，与常规方法相比，所提出的方法需要小于1/20的训练时间。此外，它通过重复训练的下层来成功执行未经学习的任务。
Recently, motion generation by machine learning has been actively researched to automate various tasks. Imitation learning is one such method that learns motions from data collected in advance. However, executing long-term tasks remains challenging. Therefore, a novel framework for imitation learning is proposed to solve this problem. The proposed framework comprises upper and lower layers, where the upper layer model, whose timescale is long, and lower layer model, whose timescale is short, can be independently trained. In this model, the upper layer learns long-term task planning, and the lower layer learns motion primitives. The proposed method was experimentally compared to hierarchical RNN-based methods to validate its effectiveness. Consequently, the proposed method showed a success rate equal to or greater than that of conventional methods. In addition, the proposed method required less than 1/20 of the training time compared to conventional methods. Moreover, it succeeded in executing unlearned tasks by reusing the trained lower layer.
