UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models
cs.RO, cs.AI, cs.CV, cs.LG
Submitted: 2026-10-13
Updated: 2026-10-19
Comments: Under review at ICLR 2027. 34 pages, 19 figures, 15 tables
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Motus: A Unified Latent Action World Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- Towards Unified World Models for Visual Navigation via Memory-Augmented Planning and Foresight
- Language-Conditioned World Modeling for Visual Navigation
- Worldscape-MoE: A Unified Mixture-of-Experts World Model for Scalable Heterogeneous Action Control
- Ctrl-World: A Controllable Generative World Model for Robot Manipulation
- OpenVLA: An Open-Source Vision-Language-Action Model
- Causal World Modeling for Robot Control
- Turning Video Models into Generalist Robot Policies
- JEPA-WAM: Learning Vision-Language-Action Policies with Joint-Embedding World Modeling
- LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
- Being-H0.7: A Latent World-Action Model from Egocentric Videos
- NavWM: A Unified Navigation World Model for Foresight-Driven Planning
- Motubrain: An Advanced World Action Model for Robot Control
- World Simulation with Video Foundation Models for Physical AI
- FACT: Failure-Aware Causal Training for World-Action Models
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies