  Autonomous manipulation in robot arms is a complex and evolving field of
study in robotics. This paper proposes work stands at the intersection of two
innovative approaches in the field of robotics and machine learning. Inspired
by the Action Chunking with Transformer (ACT) model, which employs joint
location and image data to predict future movements, our work integrates
principles of Bilateral Control-Based Imitation Learning to enhance robotic
control. Our objective is to synergize these techniques, thereby creating a
more robust and efficient control mechanism. In our approach, the data
collected from the environment are images from the gripper and overhead
cameras, along with the joint angles, angular velocities, and forces of the
follower robot using bilateral control. The model is designed to predict the
subsequent steps for the joint angles, angular velocities, and forces of the
leader robot. This predictive capability is crucial for implementing effective
bilateral control in the follower robot, allowing for more nuanced and
responsive maneuvering.
