MoWAM: Explicit Future Motion Prediction for Efficient World Action Models
cs.RO
Submitted: 2026-09-17
Updated: 2026-09-17
Terminology
Sources
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- OpenVLA: An Open-Source Vision-Language-Action Model
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
- FAST: Efficient Action Tokenization for Vision-Language-Action Models
- Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets
- World Action Models are Zero-shot Policies
- WorldVLA: Towards Autoregressive Action World Model
- Wan: Open and Advanced Large-Scale Video Generative Models
- World Simulation with Video Foundation Models for Physical AI
- Cosmos 3: Omnimodal World Models for Physical AI
- Causal World Modeling for Robot Control
- Fast-WAM: Do World Action Models Need Test-time Future Imagination?
- LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
- Octo: An Open-Source Generalist Robot Policy
- Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
- Flash-WAM: Modality-Aware Distillation for World Action Models
- Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination
- AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
- JEPA-WAM: Learning Vision-Language-Action Policies with Joint-Embedding World Modeling
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