Learn what matters: cross-domain imitation learning with task-relevant embeddings

Understand

We study how an autonomous agent learns to perform a task from demonstrations in a different domain, such as a different environment or different agent.

• Such cross-domain imitation learning is required to, for example, train an artificial agent from demonstrations of a human expert.
• We propose a scalable framework that enables cross-domain imitation learning without access to additional demonstrations or further domain knowledge.
• We jointly train the learner agent's policy and learn a mapping between the learner and expert domains with adversarial training.