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arXiv · 2211.00194

SEIL: Simulation-augmented Equivariant Imitation Learning

Mingxi Jia·Dian Wang·Guanang Su·David Klee·Xupeng Zhu·Robin Walters·Robert Platt
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Abstract

In robotic manipulation, acquiring samples is extremely expensive because it often requires interacting with the real world. Traditional image-level data augmentation has shown the potential to improve sample efficiency in various machine learning tasks. However, image-level data augmentation is insufficient for an imitation learning agent to learn good manipulation policies in a reasonable amount of demonstrations. We propose Simulation-augmented Equivariant Imitation Learning (SEIL), a method that combines a novel data augmentation strategy of supplementing expert trajectories with simulated transitions and an equivariant model that exploits the $\mathrm{O}(2)$ symmetry in robotic manipulation. Experimental evaluations demonstrate that our method can learn non-trivial manipulation tasks within ten demonstrations and outperforms the baselines with a significant margin.

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Mingxi Jia, Dian Wang, Guanang Su, David Klee, Xupeng Zhu, Robin Walters, Robert Platt. 2022-10-31. SEIL: Simulation-augmented Equivariant Imitation Learning. https://arxiv.org/abs/2211.00194

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