CRIL: Continual Robot Imitation Learning via Generative and Prediction Model

• Published in: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 6747 - 5754
• Main Authors: Gao, Chongkai, Gao, Haichuan, Guo, Shangqi, Zhang, Tianren, Chen, Feng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27.09.2021
• Subjects: Generative adversarial networks; Intelligent robots; Multitasking; Predictive models; Task analysis; Trajectory
• ISSN: 2153-0866
• DOI: 10.1109/IROS51168.2021.9636069

Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations. However, they need multi-task demonstrations to be provided at once for acquiring diverse skills, which is difficult in real world. In this work we study how to realize continual imitation learning ability that empowers robots to continually learn new tasks one by one, thus reducing the burden of multitask IL and accelerating the process of new task learning at the same time. We propose a novel trajectory generation model that employs both a generative adversarial network and a dynamics-aware prediction model to generate pseudo trajectories from all learned tasks in the new task learning process. Our experiments on both simulation and real-world manipulation tasks demonstrate the effectiveness of our method.