Cross-Domain Imitation Learning via Optimal Transport

Understand

Cross-domain imitation learning studies how to leverage expert demonstrations of one agent to train an imitation agent with a different embodiment or morphology.

• Comparing trajectories and stationary distributions between the expert and imitation agents is challenging because they live on different systems that may not even have the same dimensionality.
• We propose Gromov-Wasserstein Imitation Learning (GWIL), a method for cross-domain imitation that uses the Gromov-Wasserstein distance to align and compare states between the different spaces of the agents.
• Our theory formally characterizes the scenarios where GWIL preserves optimality, revealing its possibilities and limitations.