Graph-Based Imitation Learning in Robotics

This chapter delves into the innovative use of graph structures in imitation learning, focusing on their application with point clouds and the integration of transformer models. It discusses the generation of pseudo demonstrations in simulated environments, highlighting the scalability and efficiency of this approach in training robots on diverse tasks while managing object interactions. The chapter concludes by emphasizing the importance of combining simulated and realistic data to enhance robotic learning and task generalization.