  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.
