Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution

Understand

Visuomotor policies trained via behavior cloning are vulnerable to covariate shift, where small deviations from expert trajectories can compound into failure.

• Common strategies to mitigate this issue involve expanding the training distribution through human-in-the-loop corrections or synthetic data augmentation.
• However, these approaches are often labor-intensive, rely on strong task assumptions, or compromise the quality of imitation.
• We introduce Latent Policy Barrier, a framework for robust visuomotor policy learning.