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Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World Models

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

For robots to operate reliably in real-world environments, they need to perceive their surroundings, act, and reason about the consequences of those actions. Rapid progress in the domains of representation learning, VLA models, and world models has significantly enhanced the capabilities of robot learning systems, enabling robots to work in increasingly complex environments. However, these paradigms are typically developed in isolation, resulting in fragmented systems that struggle with generalization, long-horizon temporal reasoning and planning, and deployment in unstructured environments. I

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