生成的・予測モデルによるロボットの継続的模倣学習
CRIL: Continual Robot Imitation Learning via Generative and Prediction Model
GANとダイナミクス予測モデルで過去タスクの擬似軌道を生成し、ロボットが新タスクを逐次学習できる継続的模倣学習手法を提案した。
著者: Chongkai Gao, Haichuan Gao, Shangqi Guo, Tianren Zhang, Feng Chen
分類: cs.RO, cs.AI
原文アブストラクト
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 multi-task 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.
