CRIL: Continual robot imitation learning via generative and prediction model
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 …
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 …
[CITATION][C] Cril: Continual robot imitation learning via generative and prediction model. In 2021 IEEE
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