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Imitation learning (IL) algorithms have shown promising results for robots tolearn skills from expert demonstrations. However, they need multi-taskdemonstrations to be provided at once for acquiring diverse skills, which isdifficult in real world. In this work we study how to realize continualimitation learning ability that empowers robots to continually learn new tasksone by one, thus reducing the burden of multi-task IL and accelerating theprocess of new task learning at the same time. We propose a novel trajectorygeneration model that employs both a generative adversarial network and adynamics-aware prediction model to generate pseudo trajectories from alllearned tasks in the new task learning process. Our experiments on bothsimulation and real-world manipulation tasks demonstrate the effectiveness ofour method.

CRIL: Continual Robot Imitation Learning via Generative and Prediction Model