DIGIC: Domain Generalizable Imitation Learning by Causal Discovery
DIGIC:通过因果发现实现跨域泛化的模仿学习
Causality has been combined with machine learning to produce robust representations for domain generalization. Most existing methods of this type require massive data from multiple domains to identify causal features by cross-domain variations, which can be expensive or even infeasible and may lead to misidentification in some cases. In this work, we make a different attempt by leveraging the demonstration data distribution to discover the causal features for a domain…