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。本文针对这一挑战提出了一种创新方法，聚焦于模仿学习（IL）。与传统模仿方法不同，我们的方法采用基于双边控制的模仿学习，能够实现更精确、更具适应性的机器人运动。常规的基于双边控制的模仿学习方法依赖长短期记忆网络（LSTM）。在本文中，我们提出了基于Transformer的双边控制融合位置与力矩信息的机器人模仿学习方法（ILBiT）。该方法采用以处理多样化数据集时的稳健性能著称的Transformer模型，能够突破LSTM的局限性，尤其适用于需要精细力调节的任务。ILBiT的显著特点在于其100 Hz的高频运行能力，这显著提升了系统对不同环境及不同硬度物体的适应性与响应速度。通过全面的真实世界实验，验证了基于Transformer的ILBiT方法的有效性。