World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis
arXiv:2606.05979v1 Announce Type: cross Abstract: We propose world-language-action (WLA) models as a new class of embodied foundation models. WLA takes textual instructions, images, and robot states as inputs to jointly predict textual subtasks, subgoal images, and robot actions, conjoining the \emph{world modeling interface} to learn from extensive egocentric videos as in the world-action model (WAM) and the \emph{language reasoning} capacities to solve complex long-horizon tasks as in vision-language-action (VLA) models. At the core of WLA lies an \emph{autoregressive (AR)} Transformer backb
The rapid advancements in large language models and embodied AI are converging, enabling more sophisticated approaches to robotic intelligence and task execution.
This development proposes a unified model for robotics that combines world modeling, language reasoning, and action synthesis, significantly accelerating the path towards more capable and autonomous robots.
Current fragmented approaches to robotic intelligence are evolving towards integrated foundation models, allowing robots to interpret complex instructions and operate in diverse environments more effectively.
- · AI research institutions
- · Robotics manufacturers
- · Industrial automation sector
- · Logistics and supply chain
- · Companies relying on narrow, single-purpose robotic solutions
- · Manual labor in repetitive tasks
More versatile robots capable of understanding and executing complex, long-horizon tasks emerge.
Reduced human intervention in dangerous or labor-intensive environments, leading to efficiency gains and workforce reallocation.
The development of truly general-purpose humanoid robots becomes more feasible, impacting sectors beyond current industrial applications.
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Read at arXiv cs.AI