arXiv:2602.02259v2 Announce Type: replace Abstract: Latent action models (LAMs) offer a promising path to pre-training embodied agents on large amounts of action-free video. They infer latent actions between consecutive observations that can later be decoded to ground-truth actions using a small number of labels. However, recent work has shown that this recipe fails in the presence of action-correlated visual distractors common in real-world video, such as dynamic backgrounds, camera shake, or other moving objects. In these scenarios, the standard reconstruction objective drives latent actions
This research addresses a critical limitation in latent action models, essential for pre-training embodied AI, which is becoming more pressing as agents move into real-world, dynamic environments.
Overcoming the distraction problem in latent action models is crucial for advancing embodied AI, making systems more robust and capable of learning from diverse, uncurated video data.
Embodied AI systems can now more reliably learn generalizable skills from vast amounts of 'action-free' video, reducing the need for costly human-labeled data and improving robustness in complex environments.
- · AI research labs
- · Robotics companies
- · Embodied AI developers
- · Data collection platforms
- · Companies relying on heavily curated datasets
- · Traditional supervised learning approaches for robotics
- · Systems with high reliance on pristine sensor data
Embodied AI models become more performant and adaptable in real-world scenarios due to improved pre-training from diverse video sources.
Reduced data labeling costs accelerate the development and deployment of autonomous agents, particularly in robotics and virtual assistants.
The enhanced robustness of agentic systems could lead to increased societal integration of AI in physical and complex digital environments, influencing a wide range of industries.
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Read at arXiv cs.LG