Learning to Act Robustly with View-Invariant Latent Actions

Understand

Vision-based robotic policies often struggle with even minor viewpoint changes, underscoring the need for view-invariant visual representations.

• This challenge becomes more pronounced in real-world settings, where viewpoint variability is unavoidable and can significantly disrupt policy performance.
• Existing methods typically learn invariance from multi-view observations at the scene level, but such approaches rely on visual appearance and fail to incorporate the physical dynamics essential for robust generalization.
• We propose View-Invariant Latent Action (VILA), which models a latent action capturing transition patterns across trajectories to learn view-invariant representations grounded in physical dynamics.