What Do Latent Action Models Actually Learn?

The study of latent action models (LAMs) addresses a critical question in artificial intelligence: can these models accurately learn from unlabeled video data? By focusing on the differences between video frames, which can stem from both controllable actions and random noise, the research highlights a significant challenge in the field. The paper introduces a linear model that encapsulates the essence of LAM learning, providing valuable insights into its relationship with principal component analysis (PCA). Furthermore, it justifies various strategies, such as data augmentation and cleaning, to enhance the learning of controllable changes. The illustrative results from numerical simulations shed light on how the structure of observations, actions, and noise influences LAM learning. This research not only advances our understanding of LAMs but also has implications for improving video analysis techniques in AI.