UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models

Key points

• World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, respectively.1
• Although these models learn shared physical dynamics and interaction semantics from embodied trajectories, they are typically trained in isolation, which restricts knowledge reuse between prediction and control.1
• At deployment, a plug-in adaptive interface router (PAIR) learns from a small subset of target-task chunk scenarios to select a suitable WAM interface for each execution chunk, balancing task success and execution efficiency while keeping the backbone frozen.1
• We evaluate UniW(A)M on LIBERO, RoboTwin 2.0, and three real-world manipulation tasks, where it achieves 88.3% average real-world success versus 76.7% for the strongest baseline.1

Sources (1)

• [1]UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 04:47 PM

  We propose UniW(A)M, a mixture-of-experts world-(action) model built upon diffusion Transformers that unifies action-conditioned world modeling and diverse WAM variants.

  World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, respectively.

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