Autonomous manipulation in robot arms is a complex and evolving field of study in robotics. This paper introduces an innovative approach to this challenge by focusing on imitation learning (IL). Unlike traditional imitation methods, our approach uses IL based on bilateral control, allowing for more precise and adaptable robot movements. The conventional IL based on bilateral control method have relied on Long Short-Term Memory (LSTM) networks. In this paper, we present the IL for robot using position and torque information based on Bilateral control with Transformer (ILBiT). This proposed method employs the Transformer model, known for its robust performance in handling diverse datasets and its capability to surpass LSTM's limitations, especially in tasks requiring detailed force adjustments. A standout feature of ILBiT is its high-frequency operation at 100 Hz, which significantly improves the system's adaptability and response to varying environments and objects of different hardness levels. The effectiveness of the Transformer-based ILBiT method can be seen through comprehensive real-world experiments.

翻译：机器人手臂的自主操作是机器人学中一个复杂且不断发展的研究领域。本文通过聚焦于模仿学习（IL）提出了一种创新方法。与传统的模仿方法不同，我们的方法基于双边控制的IL，能够实现更精确、更具适应性的机器人运动。传统基于双边控制的IL方法依赖于长短期记忆（LSTM）网络。本文提出了基于变压器双边控制的位置与力矩信息机器人IL方法（ILBiT）。该方法采用Transformer模型，以其在处理多样化数据集方面的稳健性能以及超越LSTM局限性的能力而著称，特别是在需要精细力调整的任务中。ILBiT的一个突出特点是其100Hz的高频运行，这显著提升了系统对不同环境及不同硬度物体的适应性和响应能力。基于Transformer的ILBiT方法的有效性通过全面的真实世界实验得到了验证。