One-Shot Learning in Robotics

This chapter explores Instant Policies in robot learning, emphasizing efficient imitation learning through single demonstrations. It highlights the challenges of traditional methods and the evolution toward one-shot learning, focusing on the complexities of sim-to-real gaps and the role of in-context learning. By leveraging innovative graph representations and motion trajectories, the chapter illustrates how robots can swiftly acquire tasks from minimal demonstrations.