  We propose C-LAIfO, a computationally efficient algorithm designed for
imitation learning from videos, even in the presence of visual mismatch between
agent and expert domains. We analyze the problem of imitation from expert
videos with visual discrepancies, and introduce a solution for robust latent
space estimation using contrastive learning and data augmentation. Provided a
visually robust latent space, our algorithm performs imitation entirely within
this space using off-policy adversarial imitation learning. We conduct a
thorough ablation study to justify our design choices and test C-LAIfO on
high-dimensional continuous robotic tasks. Additionally, we demonstrate how
C-LAIfO can be combined with other reward signals to facilitate learning on a
set of challenging hand manipulation tasks with sparse rewards. Our experiments
show improved performance compared to baseline methods, highlighting the
effectiveness and versatility of C-LAIfO. To ensure reproducibility, we provide
open access to our code.
