Learning to Act Robustly with View-Invariant Latent Actions

• URL: http://arxiv.org/abs/2601.02994v1
• Date: Tue, 06 Jan 2026 13:14:01 GMT
• Title: Learning to Act Robustly with View-Invariant Latent Actions
• Authors: Youngjoon Jeong, Junha Chun, Taesup Kim,
• Abstract summary: Vision-based robotic policies often struggle with even minor viewpoint changes, underscoring the need for view-invariant visual representations.<n>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.<n> Experiments in both simulation and the real world show that VILA-based policies generalize effectively to unseen viewpoints and transfer well to new tasks.
• Score: 8.446887947386559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. VILA aligns these latent actions across viewpoints using an action-guided objective based on ground-truth action sequences. Experiments in both simulation and the real world show that VILA-based policies generalize effectively to unseen viewpoints and transfer well to new tasks, establishing VILA as a strong pretraining framework that improves robustness and downstream learning performance.

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