Factored Latent Action World Models
arXiv:2602.16229v1 Announce Type: new Abstract: Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning. Latent actions provide a natural interface for users to iteratively generate and manipulate videos. However, most existing approaches rely on monolithic inverse and forward dynamics models that learn a single latent action to control the entire scene, and therefore struggle in complex environments where multiple entities act simultaneously. This paper introduces Factored Latent Action Model (FLAM), a factored dynamics framework that decomposes the scene into independent factors, each inferring its own latent action and predicting its own next-step factor value. This factorized structure enables more accurate modeling of complex multi-entity dynamics and improves video generation quality in action-free video settings compared to monolithic models. Based on experiments on both simulation and real-world multi-e
arXiv:2602.16229v1 Announce Type: new Abstract: Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning. Latent actions provide a natural interface for users to iteratively generate and manipulate videos. However, most existing approaches rely on monolithic inverse and forward dynamics models that learn a single latent action to control the entire scene, and therefore struggle in complex environments where multiple entities act simultaneously. This paper introduces Factored Latent Action Model (FLAM), a factored dynamics framework that decomposes the scene into independent factors, each inferring its own latent action and predicting its own next-step factor value. This factorized structure enables more accurate modeling of complex multi-entity dynamics and improves video generation quality in action-free video settings compared to monolithic models. Based on experiments on both simulation and real-world multi-entity datasets, we find that FLAM outperforms prior work in prediction accuracy and representation quality, and facilitates downstream policy learning, demonstrating the benefits of factorized latent action models.
Executive Summary
The article introduces the Factored Latent Action Model (FLAM), a novel framework for learning latent actions from action-free videos. FLAM decomposes scenes into independent factors, each with its own latent action, enabling more accurate modeling of complex multi-entity dynamics. The model outperforms prior work in prediction accuracy and representation quality, facilitating downstream policy learning. Experiments on simulation and real-world datasets demonstrate the benefits of factorized latent action models, showcasing improved video generation quality and enhanced control over complex environments.
Key Points
- ▸ Introduction of the Factored Latent Action Model (FLAM) framework
- ▸ Decomposition of scenes into independent factors for improved modeling of complex dynamics
- ▸ Outperformance of prior work in prediction accuracy and representation quality
Merits
Improved Modeling of Complex Dynamics
FLAM's factorized structure enables more accurate modeling of complex multi-entity dynamics, leading to improved video generation quality and enhanced control over complex environments.
Demerits
Increased Computational Complexity
The factorized structure of FLAM may introduce increased computational complexity, potentially limiting its application in real-time or resource-constrained settings.
Expert Commentary
The introduction of FLAM marks a significant advancement in the field of controllable world model learning. By decomposing scenes into independent factors, FLAM enables more accurate modeling of complex multi-entity dynamics, which is essential for applications like video generation, editing, and manipulation. The factorized structure of FLAM also facilitates downstream policy learning, making it a promising framework for various applications. However, further research is needed to address potential limitations, such as increased computational complexity, and to explore the full potential of FLAM in real-world settings.
Recommendations
- ✓ Further investigation into the scalability and efficiency of FLAM in large-scale, real-world applications
- ✓ Exploration of potential applications in areas like autonomous systems, robotics, and smart cities, where accurate modeling and control of complex dynamics are crucial.
