World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis
Summary
This paper introduces World-Language-Action (WLA) models, embodied foundation models that jointly predict textual subtasks, subgoal images, and robot actions from text, images, and robot states, achieving state-of-the-art multi-task and long-horizon learning in simulated and real-world environments.
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Paper page - World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis
Source: https://huggingface.co/papers/2606.05979 Authors:
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Abstract
World-language-action models combine textual instruction processing with robot state prediction through an autoregressive transformer backbone, enabling efficient long-horizon task execution and cross-embodiment learning.
We propose world-language-action (WLA) models as a new class ofembodied foundation models. WLA takes textual instructions, images, and robot states as inputs to jointly predict textual subtasks, subgoal images, and robot actions, conjoining theworld modeling interfaceto learn from extensiveegocentric videosas in theworld-action model(WAM) and thelanguage reasoningcapacities to solve complex long-horizon tasks as in vision-language-action (VLA) models. At the core of WLA lies an autoregressive (AR) Transformer backbone, instead of a bidirectional diffusion Transformer as in WAMs, to predict the next state, comprising thesemantic-level textual intentionand complementaryfine-grained physical dynamics. The physical dynamics are supervised by theworld modeling objectivebased on a dedicated World Expert, and are leveraged to ease the characterization of the state-action correlation for theAction Expert. WLA leveragesmeta-queriesto make the world prediction implicitly impact the action generation so that the former can be disabled during inference. The world prediction can also be activated to enabletest-time scalingfor improved robot control. Our WLA-0 prototype, with 2B active parameters, achieves 40 ms per inference on an NVIDIA RTX 5090. Evaluations across simulated and real-world environments demonstrate that WLA-0 achieves state-of-the-art multi-task and long-horizon learning abilities, e.g., 92.94\% success rate on RoboTwin2.0 Clean and 56.5\% success rate on RMBench. WLA-0 also holds the promise to learn novel tasks directly fromcross-embodiment robot videoswithout action annotations.
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